# Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep
Learning Approach

Banafsheh Adami and Nima Karimian
West Virginia University
Computer Science and Electrical Engineering
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Contactless fingerprint recognition offers a higher level of user comfort and
addresses hygiene concerns more effectively. However, it is also more
vulnerable to presentation attacks such as photo paper, paper-printout, and
various display attacks, which makes it more challenging to implement in
biometric systems compared to contact-based modalities. Limited research has
been conducted on presentation attacks in contactless fingerprint systems, and
these studies have encountered challenges in terms of generalization and
scalability since both bonafide samples and presentation attacks are utilized
during training model. Although this approach appears promising, it lacks the
ability to handle unseen attacks, which is a crucial factor for developing PAD
methods that can generalize effectively. We introduced an innovative anti-
spoofing approach that combines an unsupervised autoencoder with a
convolutional block attention module to address the limitations of existing
methods. Our model is exclusively trained on bonafide images without exposure
to any spoofed samples during the training phase. It is then evaluated against
various types of presentation attack images in the testing phase. The scheme
we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for
presentation attacks involving various types of spoofed samples.

##  1 Introduction

Biometric systems have found extensive utility across various domains,
including but not limited to law enforcement and forensics, singular
identification, healthcare, and facilitating access control for smartphones
and tablets. These applications contribute to enhanced convenience in our day-
to-day activities. The demand for contactless biometric solutions is
increasing rapidly due to hygiene-related issues. Fingerprints and facial
biometrics are recognized as the primary modalities in the field of biometrics
which extensive implementation by law enforcement agencies and national ID
programs on a global scale [1]. According to the biometric system market is
projected to reach a value of $82.9 billion by 2027 [2]. Despite popularity of
face authentication, it has encountered challenges during the pandemic,
particularly regarding the use of face coverings [3], which prevents its high
rate of detection [4, 5, 6]. Contactless fingerprint recognition provides
great potential in various applications, offering a touchless and hygienic
biometric authentication solution. Contactless fingerprinting is a cutting-
edge technological advancement in the field of biometrics that eliminates the
need for traditional bioscanner sensors [7, 8, 9, 10, 11]. Instead, it relies
solely on a smartphone camera lens for capturing and recording fingertip
information. Compared to touch based fingerprint, it is considered to be more
seamless and convenient and has a higher user acceptance.

Figure 1: Different spoofed samples from CLARKSON and COLFISPOOD datasets were
employed in this paper. First four spoof samples are related to CLARKSON
dataset (WOODGLUE,PLAYDOH, PHOTOPAPER, ECOFLEX), and others are spoof samples
from COLFISPOOF dataset.

Although contactless fingerprint technology provides convenient and widely
accepted user experiences, it does come with various drawbacks. These include
lower biometric performance, susceptibility to environmental influences, and
vulnerabilities to presentation attacks [12]. Presentation attacks can
compromise the security and reliability of biometric authentication systems,
potentially leading to unauthorized access or identity theft [13]. Hence,
developing an effective countermeasure against contactless fingerprint is
crucial to detect and prevent any unseen presentation attacks. Contactless
fingerprint systems used smartphones for capturing photo-based finger images
are more vulnerable to spoofing due to the use of a single type of camera and
limited computational capabilities (Figure. 1 shows fingerprint spoof images
fabricated for this study). While well-studied has been conducted on contact-
based fingerprint recognition and its vulnerabilities to presentation attacks,
limited attention has been given to studying contactless fingerprint
presentation attacks. In recent years, only few numbers of approaches have
been explored to identify various contactless fingerprint presentation attacks
using hand-crafted features, and deeply learned features. Despite significant
progress in contactless fingerprint PAD, there are still limitations
associated with existing methods. These limitations include:

  * •

Disparities in data distributions: existing PAD approaches on contactless
fingerprint assume similar data distributions between training and testing
scenarios. However, this assumption leads to limited generalization
capabilities of PAD methods when faced with real-world situations, especially
with unseen attacks.

  * •

Multiple types of presentation attacks: contactless fingerprint presentation
attacks can take various forms, including printed attack, latex, ecoflex, and
more. Thus, generating and creating a labeled training set that covers all
possible presentation attacks for each new application scenario is
impractical.

Author | Year | Method | Database | Spoof type | Results
---|---|---|---|---|---
Tanej et al. [14] | 2016 | Hand crafted |  | IIITD:
---
class: 128
images: 5100
| Print Attack
---
Photo Attack
EER = 3.71%
Wasnik et al. [15] | 2018 |  | Hand crafted LBP,
---
BSIF, HOG, SVM
| subjects: 50
---
images: 250
videos: 150
| print artefact
---
electronic replay
elctronic display
| BPCER = 1.8, 0, 0.66,
---
APCER = 10
Fujito et al. [16] | 2018 | AlexNet |  | Live: 4096
---
spoofe sample: 8192
| Print Attack
---
Photo Attack
HTER = 0.04%
Marasco et al. [17], [18] | 2022 |  | AlexNet DenseNet201,
---
ResNet18,DenseNet121,
ResNet34, MobileNEt-V2
IIITD |  | Print Attack
---
Photo Attack
| D-EER_AlexNet = 2.14
---
D-EER_ResNet = 0.96%
Kolberg et al. [12] | 2023 | Not Reported |  | COLFISPOOF:
---
7200 spoof samples
72 different PAI
Not Reported |  | Knetosil, Mould glue,
---
latex, silly putty,
paper printout, s
chool glue,
dragonskin,
ecoflex, gelatin,
glue, modelling clau,
playdoh
Purnapatra et al. [19] | 2023 |  | DenseNet 121,
---
NASNet
| 35 subjects with 12 devices
---
attack sample: 7548
synthetic: 10000
| ecoflex, playdoh,
---
wood glue,
synthetic, fingerphoto,
latex
| APCER = 0.14%
---
BPCER = 0.18%
Hailin Li et al.[20] | 2023 |  | AlexNet,DenseNet201,
---
MobileNet-V2,ResNet50
NasNet, GoogleNet,
EfficientNEt-B0
Vision Transformer
| 5886 bonafide
---
4247 attack sample
four PAIs types
| ecoflex, playdoh,
---
wood glue,
synthetic, fingerphoto,
latex
| They report APCER and BPCER
---
in 4 cases,
in each case one PAI
used only for testing
and three remains
used for training
Puranpatra et.al.[21] | 2023 |  | Combination of
---
two CNN
| 5886 bonafide
---
4247 attack sample
four PAI types
| ecoflex, playdoh,
---
wood glue,
fingerphoto,
latex
| BPCER = 0.62
---
APCER = 11.35
ACER = 6
B Adami et al.[22] | 2023 |  | Resnet-18/LeakyRelu,
---
(Combined Loss)
| 5886 bonafide
---
4247 attack sample
10,000 synthetic
| ecoflex, playdoh,
---
wood glue,
synthetic, fingerphoto,
latex
| BPCER = 0.12
---
APCER = 0.63
ACER = 0.68
Our work | 2024 |  | convolution autoencoder,
---
CBAM-autoencoder
(proposed work),
Swin-transformer
| 35 subjects with 12 devices
---
attack sample: 7548
synthetic: 10000
| ecoflex, playdoh, wood glue,
---
synthetic, fingerphoto, latex
| APCER = 1.6%
---
BPCER = 0.96%

Table 1: Summary of previous works for contactless fingerprint anti-spoofing.
HOG– histogram of oriented gradients (HOG), SVM– support vector machine,
LBP–local binary patterns, BSIF–binarized statistical image features, EER –
equal error rate, TAR – true acceptance rate, FAR – false acceptance rate
BPCER–bonafide presentation classification error rate, HTER – half total error
rate, APCER– attack presentation classification error rate.

In this paper, we have conducted generalized PAD and introduced a novel end-
to-end trainable PAD approach called Unsupervised deep auto-encoder to detect
the unlabeled and unseen presentation attack and build a robust PAD model. The
main contributions of this paper are summarized:

  * •

A novel unsupervised convolutional block attention module auto-encoder
approach that is able to leverage bonafide (live data) data to build robust
PAD model which is independent from unseen spoofed data.

  * •

Developing advanced deep learning architecture based on live image and test
against unseen spoofed samples from two public database to improve on the
existing methods in noncontact-fingerprint PAD.

  * •

Developing an deep architecture which is able to successfully classify unseen
live subject as live samples.

  * •

Evaluating the performance of Presentation Attack Detection (PAD) in several
contactless fingerprint spoofed database tests and compared it to a supervised
learning approach where the detection of unseen attacks was not possible.

The paper organizes as follows. Section 2 discusses the previous works and
Section 3 discusses potential attacks while introducing our system
environment. Section 3 presents our new unsupervised deep learning
architecture that can detect any unseen attack. Section 4 shows the
experimental setup and results. The comparison of proposed work with existing
literature has been demonstrated in Section 4. Finally, we conclude this paper
in Section 5.

##  2 Related work

As mentioned earlier, only a few studies have been conducted to develop a
contactless fingerprint PAD (Presentation Attack Detection) system. Table. 1
provides a concise summary of the previous works related to contactless
fingerprint technology. Fujio et al. [16] were among the pioneers in exploring
the application of deep neural networks for contactless fingerprint anti-
spoofing. They achieved an impressive half-error rate of only 0.04%. Marasco
et al. [17] employed Convolutional Neural Network (CNN) architectures like
ResNet and AlexNet on the IIITD Spoofed Finger Photo Database. They achieved a
Detection Equal Error Rate (D-EER) of 2.14% for AlexNet and 0.96% for ResNet.
Subsequently, they made slight improvements in comparison to the baseline
approach[18]. Despite the promising D-EER results for the ResNet architecture,
it’s important to note that the model was trained on both live and spoofed
images, which might not be fully representative of real-world scenarios and
could impact scalability. Furthermore, in 2022, they introduced a method to
enhance the PAD system’s robustness against color paper print-out attacks
[23]. Their proposed framework involves segmenting input photos using a U-Net
with a ResNet-50 backbone. Minutiae points extracted from the images are
projected into multiple color spaces, and patches around these points are
generated to enhance local texture information. Their efforts led to an
impressive achievement of APCER (Attack Presentation Classification Error
Rate) at 0.1% when BPCER (bonafide Presentation Classification Error Rate) was
2.67%. Kolberg et al. [12] introduced the COLFISPOOF dataset specifically
designed for non-contact fingerprint Presentation Attack Detection (PAD)
purposes. This comprehensive dataset comprises 7200 samples, covering 72
distinct types of spoofed attacks, all captured using two different smartphone
devices. Purnapatra et al. [19] introduced the utilization of DenseNet-121 and
NasNetMobile models in conjunction with a newly accessible public database.
They incorporated both live and spoof data in their training process and
achieved an APCER of 0.14% and a BPCER of 0.18%. In the study by Hailin Li et
al. [20], the effectiveness of presentation attack detection (PAD) was
showcased using several models, including AlexNet, DenseNet-201, MobileNet-V2,
NASNet, ResNet50, and Vision Transformer. The vision transformer achieves the
best APCER and BPCER among other methods, which were previously used in many
fields, such as image compression [24]. The research encompassed a
comprehensive approach, involving over 5,886 genuine samples and 4,247 spoof
samples. Four distinct training cases were considered, each focusing on a
single type of spoof for testing (ecoflex, photo paper, playdoh, and wood
glue). Notably, the ResNet50 model achieved an 8.6% equal error rate (EER).
Despite the promising performance exhibited by these recent models in
recognizing fraudulent images during training, their ability to generalize to
new counterfeit images proved limited, resulting in suboptimal performance in
such scenarios. Puranpatra et al. [21] held a competition on fingerprint
liveness detection. The winner of competiton achieve APCER=9.20% for paper
printout, APCER=0% for ecoflex, playdoh and latex, APCER = 0.1% for woodglue
and APCER=99.9% for synthetic fingertip at BPCER=0.62%.

##  3 Proposed Method

Figure 2: proposed convolutional block attention module autoencoder. a)
convolutional block attention module (CBAM). b) channel attention module
(CAM), c) spatial attention module (SAM), d) convolutional autoencoder with
attention mechanism.

According to the Figure. 2, the proposed method aims to enhance feature
representation of live samples by incorporating attention modules into the
convolutional autoencoder (CAE). The CAE contains symmetric encoder and
decoder [25]. The convolutional block attention module (CBAM) has incorporated
into a CAE to achieve a better spoof detection.

Convolutional layers in autoencoder efficiently learn spatial representation
and extract meaningful features from live finger images [26] [27] [28]. We
enhance the classification performance of unsupervised model by incorporating
attention mechanism into convolutional autoencoder [29] which has been used
previously in many fields [30] [31]. The CBAM consists of two attention
mechanisms: the channel attention module (CAM) and the spatial attention
module (SAM).

The channel attention module captures inter dependencies between feature
channels of input live finger images and calculates as follows:

$\displaystyle M_{channel}(X)=\sigma(MLP(AvgPool(X))$ (1)
$\displaystyle+MLP(MaxPool(X))),$ (2)

where $\sigma$ is sigmoid activation function, $MLP$ is multi-layer
perceptron. The spatial attention module focuses on capturing the spatial
relationships within the feature maps of live input finger images [32]. The
spatial attention module, as shown in Figure 2-c, utilizes the spatial
relationships of live features to determine the most informative parts of the
live input features. This enhancement aims to improve the model’s performance
in classification tasks. The module enables the model to focus on various
spatial regions within the feature maps, thereby concentrating on the
informative regions of the input live features [32]. By attending to relevant
spatial locations of live features, the model can emphasize fine-grained
details and capture spatial relationships more effectively [33]. To calculate
the spatial attention of the live input feature, we perform average-pooling
and max-pooling operations along the channel dimension of the live feature
map. This generates a concise and informative representation of the features,
which are then concatenated. After concatenating the average-pooling and max-
pooling results, we apply convolution layers ($f^{7\times 7}$) to build a
spatial attention map ($M_{s}\in\mathbf{R}^{1\times H\times W}$). This spatial
attention mechanism aids in preserving spatial information during the encoding
and decoding processes. Equation 3 shows how we calculate spatial attention of
live features:

$\displaystyle M_{spatial}(X)=\sigma(f^{7\times 7}[AvgPool(X);MaxPool(X)]),$
(3)

This integration enables the capture of both channel-wise and spatial-wise
attention, facilitating the learning of representations for live finger
images.

We applied CAM and SAM in a sequential manner [32]. By separating the channel
attention map and spatial attention map of the live finger features, we enable
a greater emphasis on the significant and salient features within the input
live features. Furthermore, in the attention module, we have integrated global
pooling to harness comprehensive global spatial information [32] [29].

Assume $X$ is an input live finger feature, and $X\in\mathbf{R}^{C\times
H\times W}$, it deduce 1D channel attention map ($M_{c}\in\mathbf{R}^{C\times
1\times 1}$), and 2D spatial channel attention map
($M_{s}\in\mathbf{R}^{1\times H\times W}$), where $C$ is channel, $H$ is
height, and $W$ is width. Figure. 2-a, shows the whole attention process, and
we can summarize them to below equations:

$\displaystyle X^{\prime}=M_{c}(X)\otimes(X),$ (4) $\displaystyle
X^{\prime\prime}=M_{s}(X^{\prime})\otimes(X^{\prime}),$ (5)

where, $M_{c}$ and $M_{s}$ are channel and spatial attention module.
$X^{\prime}$ and $X^{\prime\prime}$ are refined output after applying channel
and spatial attention module, and $\otimes$ is hadamard multiplication.

Figure. 2 shows the proposed attention-based CAE model. As shows in Figure. 2,
we apply channel attention module and spatial attention module after each
convolution layer in both encoder and decoder to better capture and exploit
the inherent structure and dependencies present in the input live features.
The CBAM module with integrating attention mechanisms to autoencoder, enabled
the network to simultaneously focus on spatially important regions and
channel-wise features. Ultimately, the proposed attention-based convolutional
autoencoder model is an unsupervised approach and it trained only on live
samples and becomes capable of distinguishing between live and spoof finger
images. The proposed method tries to minimize the mean squared error (MSE),
which measures the pixel-wise difference between the reconstructed live image
and the original input live image [34]. The reconstruction error can provide
valuable insights into the quality of reconstructed output and latent space
representation.

###  3.1 Implementation Details

The proposed method follows an unsupervised training approach, utilizing a
dataset of unlabeled live images to train the attention based CAE model. The
unsupervised nature of the training allows the model to learn meaningful
representations without requiring explicit class labels. We only use live data
as input images and feed them into our proposed model. According to Equation
6, after each convolutional layer, we applied a rectified linear unit (Relu
activation function) and then pass the output of the convolutional layer to
the CAM (Equation 7) followed by spatial attention (Equation 8). As can be
seen in Figure. 2, the proposed encoder and decoder consist of 5 convolutional
layers and 5 spatial and channel attention layers with a kernel size of 4. The
idea behind the small kernel size is to better capture temporal features from
input data. After each CBAM we apply a dropout layer with the rate of 0.5.
After each CBAM, we apply a dropout layer with a rate of 0.5. The idea behind
applying the dropout layer in the proposed method is to prevent overfitting
and generalize better to unseen types of spoof attacks. Equation 6, 7, 8 shows
how our algorithm works. We follow this steps in both encoder and decoder part
of attention based convolutional autoencoder to find the best refined output
and minimize the reconstruction loss of live input images during training.

$\displaystyle Conv(x^{(C\times H\times W)})\rightarrow x^{(C^{\prime}\times
H^{\prime}\times W^{\prime})},$ (6)

$x^{\prime(C^{\prime}\times H^{\prime}\times W^{\prime})}\rightarrow
M_{c}(x^{(C^{\prime}\times H^{\prime}\times W^{\prime})})\otimes
x^{(C^{\prime}\times H^{\prime}\times W^{\prime})},$ (7)

$x^{\prime\prime(C^{\prime}\times H^{\prime}\times W^{\prime})}\rightarrow
M_{s}(x^{\prime(C^{\prime}\times H^{\prime}\times W^{\prime})})\otimes
x^{\prime(C^{\prime}\times H^{\prime}\times W^{\prime})},$ (8)

In equations 6, 7 and 8 values are as follows: $X$, $C$, $H$, $W$ are input
feature, channel, height and width. $x^{\prime}$, $C^{\prime}$, $H^{\prime}$,
$W^{\prime}$ are output feature, channel, height and width after applying
channel attention module. $x^{\prime\prime}$, $C^{\prime\prime}$,
$H^{\prime\prime}$, $W^{\prime\prime}$ are output feature, channel, height,
and width after applying spatial attention module. Lastly, $\otimes$ is
hadamard multiplication.

Once we obtain the reconstructed image, we calculate the MSE loss value (See
Equation 9).

$\displaystyle
MinL(x)=\frac{1}{N}\sum_{i=1}^{N}\left\|X_{i}-\hat{X}_{i}\right\|^{2},$ (9)

In above equation, $L$ is the MSE loss value, $N$ is the total number of
samples, $X_{i}$ is the reconstruction value for live images and $\hat{X}_{i}$
is the reconstruction value for test images (live/spoof). The MSE provides a
measure of how well the proposed model is able to reconstruct the input image.
A lower MSE indicates better reconstruction, as it signifies a smaller average
difference between the original live and reconstructed live pixel values. The
reconstructing error is considered as the classification threshold to classify
images as live or spoof. During unsupervised training, the model minimizes the
mean squared error (MSE) loss, which measures the pixel-wise difference
between the reconstructed image and the original input image. The Adam
optimizer is employed to optimize the model’s parameters.

##  4 Experimental Setup

###  4.1 Database

In our study, we utilized three public database including CLARKSON [19],
COLFISPOOF [12], and IIITD Spoofed Fingerphoto Database [35, 14]. The CLARKSON
dataset consists of 7,500 images of four-finger attacks, along with over
14,000 manually segmented images of single-fingertip attacks. Additionally,
there are 10,000 synthetic fingertip images created using deepfake techniques.
The dataset was gathered from six different Presentation Attack Instruments
(PAI) spanning three levels of difficulty. Furthermore, the CLARKSON database
comprises a total of 31,702 images of 26 subjects recorded from live finger
photo. Among them, 2,150 images were collected from four-finger scenario,
while 7,768 collected from single fingertip. The evaluation each device’s
effectiveness and performance involved the use of six different smartphone:
iPhone x, iPhone 7, Samsung Galaxy S9, Google Pixel, Samsung Galaxy S6, and
S7. For the spoofed image, different smartphone is utilized to generate
spoofed fingertip such as synthetic, Ecoflex PAI, Playdoh PAI, Wood Glue PAI,
Finger Photo PAI, and Latex PAI. In contrast, COLFISPOOF [12] database
contains only spoof images from different categories including dragonskin,
ecoflex, gelafix, gelatin, glue, knetosil, latex, modelling-clay, moduldable-
glue, paper-printout, playdoh, and silly-putty. Table. 2 and 3 shows the
statistics of the databases. The IIITD contains images of spoofed
fingerphotos. These images were captured using devices like the OnePlus One
and Nokia phones, involved devices such as the iPad, Laptop, Nexus, and
printouts. The dataset comprises two categories of fake samples: photo paper
and printed paper spoofs, obtained by associating 64 subjects with two
distinct fingers each where 2,048 images are print attacks, and 6,144 images
are for photo attacks. Additionally, the images were taken under varying
lighting conditions and against two different background variations. Note
that, CLARKSON dataset in this study contains less number of samples both live
and spoofed compared the one reported in 2 for the purpose of competition. The
original database has synthetic spoofed sample which has not been investigated
in this study. Based on the results reported in 2, PAD for synthetic data is
not difficult compared to photo paper.

SPOOF
---
API | NUMBER of IMAGES
ECOFLEX | 1248
PHOTOPAPER | 1104
PLAYDOH | 1700
WOODGLUE | 272
LIVE (26 subjects)
LIVE | 5886
Table 2: Statistics of CLARKSON dataset[19] Spoof | Number of Images
---|---
DRAGONSKIN | 1700
ECOFLEX | 300
GELAFIX | 100
GELATIN | 100
GLUE | 200
KNETOSIL | 200
LATEX | 100
MOULDABLE-CLAY | 100
MOULDABLE-GLUE | 900
PAPER PRINTOUT | 1200
PLAYDOH | 1700
SILLY-PUTTY | 600
Table 3: Statisitcs of the COLFISPOOF dataset[12]

###  4.2 Metrics and Evaluation Protocol

For testing the algorithm on both CLARKSON and COLFISPOOF database, three
metrics has been defined and used, bonafide presentation classification error
rate (BPCER) refers to the proportion of genuine presentations that are
incorrectly classified as attack presentations, attack presentation
classification error rate (APCER) refers to the proportion of spoofed image
that are incorrectly classified as bonafide presentation, and average
classification error rate (ACER) which refers to average of APCER and BPCER
for the comparison. Moreover, we utilized receiver operating characteristic
(ROC) curve see the performance of our model in classification between live
and spoof data samples. As we discussed in previous sections, we implemented
unsupervised training, exclusively using the CLARKSON live dataset to train
the model. Subsequently, we evaluated the model’s performance using both the
Live dataset (CLARKSON) and the SPOOF dataset (CLARKSON, CLFISPOOF). Firstly,
we split the live dataset into training and testing sets, ensuring that all
the live subjects were included in both sets. Secondly, we implemented K-fold
cross-validation to estimate the performance of our model on unseen live
subjects [36]. In each fold, we allocated 80% of the subjects for training and
20% for testing. In the subsequent sections, we will present the results for
each training method.

###  4.3 Experimental Results

We have implemented three methods including, CAE, CAAE, and swin transformer
for contactless fingerprint anti-spoofing detection purpose. CAE, contains 5
convolution layers in both encoder and decoder, where each convolution layer
consist of rectified linear unit (ReLU) activation function followed by
sigmoid function in the last layer of decoder. We set the kernel size to 4,
with stride = 2. We set the live input image size to 256 $\times$ 256 with 3
channels (RGB), and reduce the dimension to latent representation 4 $\times$ 4
with 2048 channel. Finally, the decoder reconstruct the image with same size
as input live image. To evaluate the model we use both live and spoof dataset
(See Table. 5, for the convolutional autoencoder results). CAE is a proper
architecture to learn the representation of input live data, however, in order
to improve the classification accuracy, we applied attention mechanism to the
convolutional autoencoder. As we discussed in section 3, we applied channel
attention and spatial attention on convolutional autoencoder to capture the
inter-dependencies between different channels of the live input feature maps
and focusing on capturing the inter-dependencies between spatial locations
within each channel of the feature maps and finally improve the model
performance in classification. We incorporated the swin transformer into our
classification approach to enhance its capabilities. Based on the data
presented in Table 5, our CBAM-autoencoder model demonstrates superior
performance across various types of spoof samples from CLARKSON, yielding an
average APCER of 1.6%. Notably, for COLFISPOOFED and IIITD samples, the APCER
is 0%. Additionally, the BPCER is measured at 0.96%, contributing to an ACER
value of 1.28%, outperforming the two alternative approaches. Finally, in
Figure. 3 we plot bonafide presentation classification error rate (BPCER) to
attack presentation classification error rate (APCER) to shows the error rate
of three implemented methods. According to Figure. 3, the CBAM-autoencoder
error rate reduced than the convolutional autoencoder and swin transformer.

Figure 3: ROC curve of three different deep learning approach for PAD Fold_number | BPCER(%) | TPR(%)
---|---|---
Fold_1 | 3.7 | 96.28
Fold_2 | 1.9 | 98.56
Fold_3 | 1.3 | 98.68
Fold_4 | 4.1 | 95.87
Fold_5 | 2.03 | 97
Table 4: K-fold Cross Validation on Live CLARKSON dataset

ClARKSON
---
Method | DL |  | TRAINING
---
SAMPLES
| TESTING
---
SAMPLES
APCER% |  BPCER % |  HTER %
LIVE | SPOOF | LIVE | SPOOF | ECO | PH | PL | WO
DENSENET-121 [19] | S | 55,624 | 5,590 | 0 | 88.03 | 0.14 | 0 | 0.18 | NA
DENSENET-121(KERAS) [19] | S | 55,624 | 5,590 | 0 | 79.01 | 1.55 | 0.94 | 3.64 | NA
NASNETMOBILE [19] | S | 55,624 | 5,590 | 0 | 82.15 | 0.71 | 5.96 | 9.04 | NA
DENSENET-121(GRAYSCALE) [19] | S | 55,624 | 5,590 | 0.16 | 98.9 | 1.98 | 11 | 0.18 | NA
CASE-1 | RESNET50 [20] | S | NA | 2099 | NA | 1248 | 13.32 | - | - | - | 3.33 | 6.9
DENSNET201 [20] | S | NA | 2099 | NA | 1248 | 15.33 | – | - | - | 3.33 | 7.61
EFFICIENTNET-B0 [20] | S | NA | 2099 | NA | 1248 | 16.72 | - | - | - | 3.33 | 8.07
CASE-2 | RESNET50 [20] | S | NA | 3143 | NA | 1104 | - | 45.33 | - | - | 3.33 | 17.61
DENSNET201 [20] | S | NA | 3143 | NA | 1104 | - | 94.72 | - | - | 3.33 | 34.07
EFFICIENTNET-B0 [20] | S | NA | 3143 | NA | 1104 | - | 100 | - | - | 3.33 | 38.89
CASE-3 | RESNET50 [20] | S | NA | 2624 | NA | 1623 | - | - | 17.34 | - | 3.33 | 8.28
DENSNET201 [20] | S | NA | 2624 | NA | 1623 | - | - | 100 | - | 3.33 | 50
EFFICIENTNET-B0 [20] | S | NA | 2624 | NA | 1623 | - | - | 100 | - | 3.33 | 38.89
CASE-4 | RESNET50 [20] | S | NA | 3975 | NA | 272 | - | - | - | 0.39 | 3.33 | 2.63
DENSNET201 [20] | S | NA | 2975 | NA | 272 | - | - | - | 5.97 | 3.33 | 4.49
EFFICIENTNET-B0 [20] | S | NA | 2975 | NA | 272 | - | - | - | 1.74 | 3.33 | 3.08
AUTOENCODER+CBAM (OURS) | U | 4208 | 0 | 1678 | 4324 | 0 | 29.35 | 0 | 0 | 0.96 | 3.43
AUTOENCODER(OURS) | U | 4208 | 0 | 1678 | 4324 | 0 | 67.3 | 0 | 0.73 | 1.92 | 4.79
SWIN TRANSFORMER(OURS) | U | 4208 | 0 | 1678 | 4324 | 37.1 | 73.28 | 0 | 1.11 | 0.96 | 5.9
COLFISPOOOF |
METHOD | DL |  | TRAINING
---
SAMPLES
| TESTING
---
SAMPLES
APCER% |  BPCER % |  HTER %
LIVE | SPOOF | LIVE | SPOOF | ECO | GEL1 | GEL2 | GLUE1 | KNE | LA | CLAY | GLUE2 | PA | PL | SL | DR
AUTOENCODER(OURS) | U | 4208 | 0 | 1678 | 7200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.92 | 0.96
AUTOENCODER+CBAM (OURS) | U | 4208 | 0 | 1678 | 7200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.96 | 0.48
SWIN TRANSFORMER(OURS) | U | 4208 | 0 | 1678 | 7200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.96 | 0.48
IIITD DATASET
METHOD | DL |  | TRAINING
---
SAMPLES
| TESTING
---
SAMPLES
| APCER
---
%
BPCER % |  HTER %
LIVE | SPOOF | LIVE | SPOOF | PA | PH
ALEXNET [16] | S | NA | NA | NA | NA | NA | NA | NA | 0.04
RESNET18 [17] | S | NA | NA | NA | NA | NA | NA | NA | NA
DENSENET121 [18] | S | NA | NA | NA | NA | NA | NA | NA | 1.274
AUTOENCODER+CBAM(OURS) | U | 4208 | 0 | 1678 | 8192 | 0 | 0 | 0.96 | 0.48
AUTOENCODER(OURS) | U | 4208 | 0 | 1678 | 8192 | 0 | 0.91 | 1.92 | 1.13
SWIN TRANSFORMER(OURS) | U | 4208 | 0 | 1678 | 8192 | 0 | 43.11 | 0.96 | 16.48

Table 5: Comparison of proposed method with previous works in terms of APCER
and BPCER. DL–deep learning techniques which S is supervised and U is
unsupervised, NA— not reported. ECO–ecoflex, PH–photo paper, PA– printed
attack, PL–playdoh, WO–woodglue, DR–dragonskin, GEL1–gelatin, GEL2–gelafix,
GLUE1–GLUE, KNE–knetosil, LA–latex, CLAY–mouldableclay, GLUE2–modulableglue,
SL–silly putty.

We initially anticipated the swin transformer will improve results with this
approach. However, as indicated in Table. 5, the performance of the swin
transformer in detecting spoof datasets (photopaper and woodglue) in the
CLARKSON dataset does not surpass that of the CBAM-autoencoder. This outcome
may be attributed to the limited quality and quantity of the training dataset.
However, the bonafide classification error rate of the swin transformer
improved by 1% compared to CBAM-Autoencoder and 50% compared to convolutional
autoencoder. Based on our finding, the swin transformer successfully detect
all the different types of spoofs on the CLFISPOOF dataset as “spoof,”
achieving an APCER of 0%.

###  4.4 Discussion and Comparison

Based on the results demonstrated in Table 5, our proposed methods Auto-CBAM
achieved the best performance compared to state of art techniques [19, 20]. In
order to contrast our approach with previous research, various metrics are
considered, including supervised versus unsupervised methodologies,
differences in training and testing sample sizes, and variations in the types
of spoofed samples examined during testing. To begin with, both the studies by
Purnapatra et al. [19] (2023) and Li et al. [20] (2023) are utilized
supervised techniques, while our approach adopts an unsupervised framework.
This distinction signifies that their models were trained using both spoofed
and live samples, whereas in our methodology, we exclusively employ live
samples for training purposes. Furthermore, our evaluation diverges from
conventional approaches by assessing the performance on previously unseen
authentic subjects when calculating the BPCER. In contrast, the previous works
utilize unseen samples from trained subjects for their assessment. sing two
distinct databases, marking the first instance of such an assessment. In
contrast, existing work are only evaluated based on the Clarkson database. As
depicted in Table 5, our model’s evaluation includes 11,524 spoofed samples.
This number significantly exceeds the quantity of spoofed samples employed in
the study by Li et al. (2023) by approximately 10,000 and surpasses the count
in the research conducted by Purnapatra et al. (2023) by an additional 7,500
spoofed samples. Based on our analysis, we found photo paper is most
challenging spoofed samples compared to other types of spoofed samples. Having
said that, our model successfully shows a degradation of the error rate of the
APCER by 49.66% when compared to the performance of DenseNet-121 (Keras).
Furthermore, it demonstrates a 58.68% reduction in error rate in comparison to
standard DenseNet-121, a 69.55% decrease when contrasted with DenseNet-121
trained on grayscale data, and a 52.8% improvement over NasNetMobile, as cited
in Purnapatra et al.’s work [19]. Also, our proposed method performance in
detecting photo paper as spoof improved by 15.98% compared with ResNet50,
65.37% compared with DenseNet201, 70.65% compared with EfficientNet-BO in
[20]. Note that BPCER and APCER from Li et al. [20] were adjusted for better
comparison. We also compared our work with [16, 17, 18] using IIITD spoofed.
Similar to other comparison, we only trained our model using live samples from
CLARKSON database and evaluated under IIITD spoofed samples. As we described
in datasets, the IIITD dataset comprises two spoof samples including 2,048
images from print attacks, and 6,144 images from photo attacks. As can be seen
in Table 5, our model achieved “zero” of APCER on both printed and photo
attacks while existing work did not reported APCER. It is worth noting that,
unlike prior studies that omitted reporting the APCER, we have explicitly
included APCER reporting in table 5. We also noticed that, while our proposed
Auto-CBAM and autoencoder architecture work very well on photo paper, swing
transformer performance exhibits degradation. one reason is that, by
incorporating both channel-wise and spatial-wise attention mechanisms in CBAM,
where both global contextual information (channel-wise) and fine-grained local
details (spatial-wise) is captured will help to outperformed compared to other
model.

We also illustrated that swin transformer resulted 73.63% improvement on the
BPCER compared to DenseNet-121 (keras) [19]. While the Swin Transformer did
not outperform CBAM-Autoencoder, it still exhibited the APCER improved by
16.7% compared to DenseNet-121, 7.25% compared to DenseNet-121 (keras), 10.79%
compared to NasNetMobile, and 25.9% compared to DenseNet-121 (grayscale) for
photo paper attacks. We also implement K-fold cross-validation to evaluate the
performance of CBAM-Autoencoder model on unseen live subject data. Table. 4
demonstrates the performance of CBAM-Autoencoder with K-fold cross validation
training. As we mentioned, the CLARKSON dataset, contains 26 subjects for live
dataset. For k-fold cross validation implementation, we allocated 20% of the
unseen subjects (4 Subjects) for testing and the remaining used for training
to evaluate the performance of our model on unseen live subjects. According to
the Table. 5, our model achieved performed well in each fold, indicating that
our model is capable of classifying unseen live subjects as live.

##  5 Conclusion

Current research on presentation attack detection (PAD) primarily relies on
supervised learning techniques, where both bonafide samples and spoofed
samples are utilized during training which are not scalable due to poor
performance against unseen attack. In this paper, we introduced PAD using
unsupervised approach that combines an unsupervised autoencoder with a
convolutional block attention. Our proposed deep learning approach is only
trained on bonafide images without exposure to any spoofed samples. It is then
evaluated against unseen spoofed samples in the testing phase. The scheme we
proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for
presentation attacks with various types of spoofed samples.

##  6 Acknowledgments

This project was supported in part by the National Science Foundation under
Grants No. 2104520.

## References

  * [1] A. K. Jain, K. Nandakumar, and A. Ross, “50 years of biometric research: Accomplishments, challenges, and opportunities,” Pattern recognition letters, vol. 79, pp. 80–105, 2016.
  * [2] M. LLC, “Biometric Market Size.” https://www.fortunebusinessinsights.com/biometric-system-market-107100, 2023.  [Online; accessed 22-June-2023].
  * [3] M. Calbi, N. Langiulli, F. Ferroni, M. Montalti, A. Kolesnikov, V. Gallese, and M. A. Umilta, “The consequences of covid-19 on social interactions: an online study on face covering,” Scientific reports, vol. 11, no. 1, p. 2601, 2021.
  * [4] Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3476–3483, 2013.
  * [5] C.-W. Yang, T. H. Phung, H.-H. Shuai, and W.-H. Cheng, “Mask or non-mask? robust face mask detector via triplet-consistency representation learning,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 18, no. 1s, pp. 1–20, 2022.
  * [6] C. Botezatu, M. Ibsen, C. Rathgeb, and C. Busch, “Fun selfie filters in face recognition: Impact assessment and removal,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5, no. 1, pp. 91–104, 2022.
  * [7] S. A. Grosz, J. J. Engelsma, E. Liu, and A. K. Jain, “C2cl: Contact to contactless fingerprint matching,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 196–210, 2021.
  * [8] C. Lin and A. Kumar, “A cnn-based framework for comparison of contactless to contact-based fingerprints,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 3, pp. 662–676, 2018.
  * [9] C. Lin and A. Kumar, “Matching contactless and contact-based conventional fingerprint images for biometrics identification,” IEEE Transactions on Image Processing, vol. 27, no. 4, pp. 2008–2021, 2018.
  * [10] A. Kumar and C. Kwong, “Towards contactless, low-cost and accurate 3d fingerprint identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3438–3443, 2013.
  * [11] Z. Cui, J. Feng, and J. Zhou, “Monocular 3d fingerprint reconstruction and unwarping,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
  * [12] J. Kolberg, J. Priesnitz, C. Rathgeb, and C. Busch, “Colfispoof: A new database for contactless fingerprint presentation attack detection research,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 653–661, 2023.
  * [13] R. Tolosana, M. Gomez-Barrero, C. Busch, and J. Ortega-Garcia, “Biometric presentation attack detection: Beyond the visible spectrum,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1261–1275, 2019.
  * [14] A. Taneja, A. Tayal, A. Malhorta, A. Sankaran, M. Vatsa, and R. Singh, “Fingerphoto spoofing in mobile devices: a preliminary study,” in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–7, IEEE, 2016.
  * [15] P. Wasnik, R. Ramachandra, K. Raja, and C. Busch, “Presentation attack detection for smartphone based fingerphoto recognition using second order local structures,” in 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 241–246, IEEE, 2018.
  * [16] M. Fujio, Y. Kaga, T. Murakami, T. Ohki, and K. Takahashi, “Face/fingerphoto spoof detection under noisy conditions by using deep convolutional neural network.,” in BIOSIGNALS, pp. 54–62, 2018.
  * [17] E. Marasco and A. Vurity, “Fingerphoto presentation attack detection: Generalization in smartphones,” in 2021 IEEE International Conference on Big Data (Big Data), pp. 4518–4523, IEEE, 2021.
  * [18] E. Marasco, A. Vurity, and A. Otham, “Deep color spaces for fingerphoto presentation attack detection in mobile devices,” in International Conference on Computer Vision and Image Processing, pp. 351–362, Springer, 2021.
  * [19] S. Purnapatra, C. Miller-Lynch, S. Miner, Y. Liu, K. Bahmani, S. Dey, and S. Schuckers, “Presentation attack detection with advanced cnn models for noncontact-based fingerprint systems,” in 2023 11th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, IEEE, 2023.
  * [20] H. Li and R. Ramachandra, “Deep features for contactless fingerprint presentation attack detection: Can they be generalized?,” arXiv preprint arXiv:2307.01845, 2023.
  * [21] S. Purnapatra, H. Rezaie, B. Jawade, Y. Liu, Y. Pan, L. Brosell, M. R. Sumi, L. Igene, A. Dimarco, S. Setlur, et al., “Liveness detection competition–noncontact-based fingerprint algorithms and systems (livdet-2023 noncontact fingerprint),” arXiv preprint arXiv:2310.00659, 2023.
  * [22] B. Adami, S. Tehranipoor, N. Nasrabadi, and N. Karimian, “A universal anti-spoofing approach for contactless fingerprint biometric systems,” arXiv preprint arXiv:2310.15044, 2023.
  * [23] E. Marasco and A. Vurity, “Late deep fusion of color spaces to enhance finger photo presentation attack detection in smartphones,” Applied Sciences, vol. 12, no. 22, p. 11409, 2022.
  * [24] A. Khoshkhahtinat, A. Zafari, P. M. Mehta, M. Akyash, H. Kashiani, and N. M. Nasrabadi, “Multi-context dual hyper-prior neural image compression,” arXiv preprint arXiv:2309.10799, 2023.
  * [25] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning.  MIT press, 2016.
  * [26] P. Bedi and P. Gole, “Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network,” Artificial Intelligence in Agriculture, vol. 5, pp. 90–101, 2021.
  * [27] Y. Zhang, “A better autoencoder for image: Convolutional autoencoder,” in ICONIP17-DCEC. Available online: http://users. cecs. anu. edu. au/Tom. Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58. pdf (accessed on 23 March 2017), 2018.
  * [28] U. Michelucci, “An introduction to autoencoders,” arXiv preprint arXiv:2201.03898, 2022.
  * [29] M.-H. Guo, T.-X. Xu, J.-J. Liu, Z.-N. Liu, P.-T. Jiang, T.-J. Mu, S.-H. Zhang, R. R. Martin, M.-M. Cheng, and S.-M. Hu, “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3, pp. 331–368, 2022.
  * [30] A. M. Obeso, J. Benois-Pineau, M. S. G. Vázquez, and A. Á. R. Acosta, “Visual vs internal attention mechanisms in deep neural networks for image classification and object detection,” Pattern Recognition, vol. 123, p. 108411, 2022.
  * [31] M. A. Farahani, M. McCormick, R. Gianinny, F. Hudacheck, R. Harik, Z. Liu, and T. Wuest, “Time-series pattern recognition in smart manufacturing systems: A literature review and ontology,” arXiv preprint arXiv:2301.12495, 2023.
  * [32] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), pp. 3–19, 2018.
  * [33] L. Chen, H. Zhang, J. Xiao, L. Nie, J. Shao, W. Liu, and T.-S. Chua, “Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5659–5667, 2017.
  * [34] S. Zagoruyko and N. Komodakis, “Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer,” arXiv preprint arXiv:1612.03928, 2016.
  * [35] A. Sankaran, A. Malhotra, A. Mittal, M. Vatsa, and R. Singh, “On smartphone camera based fingerphoto authentication,” in 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–7, IEEE, 2015.
  * [36] Y. Bengio and Y. Grandvalet, “No unbiased estimator of the variance of k-fold cross-validation,” Advances in Neural Information Processing Systems, vol. 16, 2003.


# Results from ON-OFF analysis of the Neutrinos-Angra detector

E. Kemp  Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas,
Campinas, 13083-859, SP, Brazil Corresponding author<EMAIL_ADDRESS>W. V.
Santos  Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas,
Campinas, 13083-859, SP, Brazil J. C. Anjos  Centro Brasileiro de Pesquisas
Físicas, Rio de Janeiro, 22290-180, RJ, Brazil  P. Chimenti  Departamento de
Física, Universidade Estadual de Londrina, Londrina, 86057-970, PR, Brazil  L.
F. G. Gonzalez  Instituto de Computação, Universidade Estadual de Campinas,
Campinas, 13083-852, SP, Brazil  G. P. Guedes  Universidade Estadual de Feira
de Santana, Feira de Santana, 44036-900, BA, Brazil  H. P. Lima Jr  Centro
Brasileiro de Pesquisas Físicas, Rio de Janeiro, 22290-180, RJ, Brazil  R. A.
Nóbrega  Departamento de Circuitos Elétricos, Universidade Federal de Juiz de
Fora, Juiz de Fora, 36036-900, MG, Brazil  I. M. Pepe  Instituto de Física,
Universidade Federal da Bahia, Salvador, 40170-115, BA, Brazil  D. B. S.
Ribeiro  Instituto de Física, Universidade Federal da Bahia, Salvador,
40170-115, BA, Brazil

(June 26, 2024)

###### Abstract

The Neutrinos Angra Experiment, a water-based Cherenkov detector, is located
at the Angra dos Reis nuclear power plant in Brazil. Designed to detect
electron antineutrinos produced in the nuclear reactor, the primary objective
of the experiment is to demonstrate the feasibility of monitoring reactor
activity using an antineutrino detector. This effort aligns with the
International Atomic Energy Agency (IAEA) program to identify potential and
novel technologies applicable to nonproliferation safeguards.

Operating on the surface presents challenges such as high noise rates,
necessitating the development of very sensitive, yet small-scale detectors.
These conditions make the Angra experiment an excellent platform for both
developing the application and gaining expertise in new technologies and
analysis methods. The detector employs a water-based target doped with
gadolinium to enhance its sensitivity to antineutrinos.

In this work, we describe the main features of the detector and the
electronics chain, including front-end and data acquisition components. We
detail the data acquisition strategies and the methodologies applied for
signal processing and event selection. Preliminary physics results suggest
that the detector can reliably monitor reactor operations by detecting the
inverse beta decay induced by electron antineutrinos from the reactor.

##  1 Introduction

The $\nu$-Angra experiment aims to develop an antineutrino detector designed
to improve nuclear safety at the Angra dos Reis power plant in Brazil. Nuclear
reactors have been crucial to experimental neutrino physics, as they are
numerous man-made sources of neutrinos. The detection of antineutrinos
produced by nuclear power plants was first achieved in 1956 by pioneers C.L.
Cowan and F. Reines, who detected the Inverse Beta Decay reaction (IBD):
$\bar{\nu_{e}}+p\rightarrow n+e^{+}$ [1]. Since then, other experiments have
been built inside such facilities, with reactor neutrino experiments
significantly contributing to the precise knowledge of the neutrino
oscillation parameters [2, 3, 4, 5].

The proposal of using neutrinos for remote monitoring of nuclear reactor
thermal power was first considered in the mid-1970s [6, 7]. One of the first
demonstration experiments was performed in a neutrino laboratory located in
the Rovno nuclear power plant, Ukraine, where the relationship between
neutrino count rate and reactor activity was clearly shown [8, 9]. This
scenario opens up solid perspectives for using neutrinos as reliable probes of
the physical processes in which they participate, making a neutrino detector
capable of monitoring parameters related to the activity of nuclear reactors
crucial for non-proliferation safeguards dictated by the International Atomic
Energy Agency (IAEA) [10, 11].

The $\nu$-Angra experiment employs a water Cherenkov detector that utilizes a
non-flammable target positioned at the surface level. This experiment,
initially considered for studies of neutrino oscillations, evolved to focus on
the verification of nonproliferation safeguards, given the opportunity to
conduct an experiment at the Angra dos Reis nuclear complex. The Angra-II
reactor, in steady-state operation, provides a neutrino flux estimated as
$1.21\times 10^{20}\leavevmode\nobreak\ s^{-1}$ [12]. The electron
antineutrino flux can be used to perform noninvasive monitoring of reactor
activity and estimate the thermal power produced at the reactor core.

The long-term goal of the $\nu$-Angra experiment is to develop a reliable and
cost-effective technology to routinely monitor nuclear reactor power and
possibly neutrino spectral evolution, which can reveal the composition of
burned fuel, with special interest in the fraction of plutonium [13, 14]. The
stability of data acquisition is a fundamental step in this direction, and in
the commissioning phase, we have fully validated the electronics that operate
within the desired stability for years of data collection [15].

A common challenge for all neutrino experiments is the background radiation,
mainly neutrons and cosmic muons, that can mimic signals expected from
neutrino detection. To suppress background levels, detectors are usually
installed in large underground caverns using rock and soil overburdens as
natural shields. However, the $\nu$-Angra detector is assembled on the
surface, adhering to the agreements with the plant operator, which presents a
significant challenge in handling an extremely low signal-to-noise ratio with
enough ability to find genuine IBD events to monitor the Angra II nuclear
reactor.

This article is structured as follows. Initially, we describe the water
Cherenkov detector located near the Angra-II power plant. Next, we present the
data acquisition system. Subsequently, we explore the data analysis phase,
which encompasses pre-processing, data selection, and the detection of
neutrino interactions. Finally, we conclude with a discussion of our findings
and the implications of our research.

##  2 The $\nu$-Angra detector

Constructed in the 1980s near Angra dos Reis (RJ), the Angra-II nuclear power
plant operates with a nominal thermal power of 3.8 GW. The Neutrino Physics
Laboratory, housed within a standard high-cube container, is strategically
placed about 25 meters from the reactor core, just outside and close to the
containment dome of Angra-II. In contrast, Angra-I, another reactor core at
the plant, is located approximately 200 meters away, whose contribution to the
neutrino flux can be disregarded in the current context. Currently, this site
hosts two significant experiments: $\nu$-Angra and CONNIE (Coherent Neutrino-
Nucleus Interaction Experiment) [16].

The $\nu$-Angra experiment aims to develop an antineutrino detector that works
as a tool for nuclear safeguards, using a water Cherenkov detector to measure
the flux of antineutrinos emitted by the reactor. The $\nu$-Angra detector
comprises three nested volumes, each playing a different role in the detection
and mitigation of background signals. The system features a central
parallelepipedal tank, known as the target, which holds $\sim 1.3$ kL of water
doped with 0.2% gadolinium chloride (GdCl3) by mass. The target is monitored
by 32 8-inch Hamamatsu R5912 photomultipliers (PMT), with 16 installed at the
bottom and 16 at the top of the volume, as shown in Figure 1. Furthermore, the
internal faces of the target are lined with GORE®, a light-diffusing membrane
that ensures a reflectance of more than 99.0% of photons $\sim$ 400 nm
wavelength.

The electron antineutrino flux generated by the reactor can be used to perform
non-invasive monitoring of the reactor activity and estimate the thermal power
produced at the reactor core. The expected rate for the 1-ton target detector
is around 5000 events per day considering the 25 m distance from the reactor
core. This enables the experiment to investigate the potential of antineutrino
detection for safeguards applications.

In the IBD interaction, the electron antineutrino ($\bar{\nu_{e}}$) interacts
with a hydrogen nucleus (proton, $p$), producing a positron ($e^{+}$) and a
neutron ($n$): $\bar{{\nu_{e}}}+p\rightarrow e^{+}+n$. The positron promptly
produces a Cherenkov flash111In water electrons or positrons with kinetic
energies exceeding 264 keV emit Cherenkov radiation, detectable by the PMTs in
the target. and also annihilates with an electron from the detector material,
creating two ($\gamma$) photons releasing additional energy for the reaction
of approximately 511 keV each: $e^{+}+e^{-}\rightarrow\gamma+\gamma$. This
initial burst of light from the positron is termed the ’prompt signal’.
Subsequently, the neutron, after thermalization, is captured by a gadolinium
nucleus [17] within about $12.3\;\mu\text{s}$ [18], leading to the emission of
photons from deexcitation of the Gd atom. Typically, the reaction n(Gd,
Gd∗)$\gamma$ releases a total gamma energy of $\sim$ 8 MeV. This subsequent
photon emission is known as the ’delay signal’. Together, these IBD
interactions produce distinct signals separated by the ’prompt’ to ’delay’
specific times and energy signatures.

To mitigate background noise from cosmic and environmental radiation, the
detector’s target is encased within two veto subsystems: the top veto and the
lateral veto. The top veto is positioned directly above the target, while the
lateral veto encircles it. Both subsystems are filled with ultra-pure water
and each is equipped with four PMTs. The inner surfaces of these subsystems
are lined with Tyvek®, another reflective liner with an efficiency greater
than 97.0% of light at pertinent wavelengths. Activation of two PMTs
simultaneously in either subsystem is interpreted as background particles
interference, prompting the data acquisition system to stop recording for that
event.

Additionally, the lateral veto subsystem is encased in a nonactive volume
known as the ’shield,’ which features two sides measuring 14.5 cm in thickness
and two others 22.5 cm thick, all filled with water. This configuration
significantly enhances the subsystem’s ability to block neutrons from cosmic
radiation or the environment. The principal components of the $\nu$-Angra
detector are illustrated in Figure 1.

Figure 1: Exploded view of the $\nu$-Angra detector.

##  3 Readout Electronics and Data Acquisition

The readout and data acquisition system comprises several key components:
Front-End Electronics (FEE), Digitizers, and the Trigger. A diagram of the
electronics and data acquisition chain is shown in Figure 2.

Figure 2: Overview of the complete electronics chain and readout scheme
installed on the nuclear power plant laboratory.

A comprehensive data acquisition system has been developed and implemented for
the $\nu$-Angra experiment to fulfill several critical functions: biasing the
PMTs with a high voltage power supply, amplifying the output signals from the
PMTs via Front-End Electronics (FEE), sampling and digitizing the signals
through the Neutrino Data Acquisition System (NDAQ), selecting events using
the trigger system, and managing local data storage, which is mirrored at CBPF
and Unicamp. Figure 3 shows a picture of the rack with the $\nu$-Angra
electronics installed in our laboratory at the reactor site.

Figure 3:  Rack with $\nu$-Angra Detector Electronics [18].

The HV power supply system, along with the local computing infrastructure (DAQ
server) and data storage components, are commercial standard products. The HV
supply, which biases the 40 PMTs is a mainframe-based system manufactured by
CAEN, and is remotely controlled via an Ethernet connection. Local data
storage comprises a network-connected storage unit that continuously records
data from the detector. This data is subsequently transferred to two larger,
permanent data servers: the primary server located at CBPF in Rio de Janeiro,
and a secondary mirror server at Unicamp in Campinas.

###  3.1 Front-End Electronics - FEE

Custom front-end circuitry has been engineered for the $\nu$-Angra detector to
process PMT output signals for digitization by NDAQ modules and to feed the
trigger system with information from activated channels [19].

Five front-end boards (FEBs), each containing eight independent channels, are
integrated to the experiment. Each channel features a four-stage
amplification/shaper circuit that conditions the signal for digitization, a
discriminator circuit that relays outputs to the trigger system, and a control
system. The control system employs a programmable Digital-to-Analog Converter
(DAC) integrated circuit, enabling remote adjustments of the analog signal’s
offset and the discrimination thresholds according to experimental needs. The
FEE functionality of each channel is depicted in a block diagram shown in
Figure 4.

Figure 4: Block diagram of single FFE channel.

###  3.2 Digitizer

The detector signals, amplified and shaped by the Front-End Electronics (FEE),
are transmitted to the VME-based system that samples and digitizes them upon
receiving a trigger pulse. The NDAQ board was specifically designed for the
experiment [20]. A VME single-board computer serves as the readout processor
(ROP) for five NDAQ boards housed in a VME crate. The ROP manages and reads
the NDAQ cards during the data acquisition phase. Additionally, a commercial
fan-in fan-out module distributes the digital trigger pulse from the trigger
system to the NDAQ modules, ensuring synchronized data handling.

Figure 5: Block diagram of the NDAQ data acquisition board.

Each NDAQ module has eight Analog-to-Digital Converter (ADC) channels working
at a 125 MHz sampling rate (10-bit resolution), as shown in Figure 5. The ADC
output samples are sent to a Field Programmable Gate Array (FPGA), which
controls the data flow to the two FIFO memories connected in series.

###  3.3 Trigger

When an event occurs, the output signals from the Front-End Electronics (FEE)
are digitized by the NDAQ modules and stored in onboard FIFO (First-In, First-
Out) memories pending a trigger decision. If the Trigger System selects the
event [21], the data is then transferred to the DAQ server and subsequently
recorded in the data storage unit.

The trigger board processes inputs from 40 discriminated signals, including 32
PMTs from the target, 4 PMTs from the lateral Veto, and 4 PMTs from the top
Veto. An event is accepted if at least five PMT units from the Target are
triggered within a specified time window, and it is blocked if simultaneous
triggers are detected from two individual PMT units in either veto system.

Figure 6: Trigger electronics overview.

Different trigger configurations are available and can be configured by remote
access to a Raspberry PI®control card.

###  3.4 Data flow control software

To manage all data acquisition components, the detector utilizes a system
orchestrator that runs a Python script known as Run-Control on the Angra DAQ
Server. The primary role of Run-Control is to initiate data acquisition on the
Readout Processor (ROP) and trigger system. Upon receiving data, a ‘Run’ file
is created and temporarily stored in RAM with a time stamp managed by the
Redis network database. This ‘Run’ record is then formatted into an Apache
Parquet structured file. Subsequently, the raw file is transmitted to a
Network Attached Storage (NAS) system over Gigabit Ethernet, which maintains a
4 hard drives, 4TB each, configured in RAID 5. Each ‘Run’ captures 30 minutes
of event data.

Pre-processing routines handle the data files to integrate the charge data for
each PMT, enabling fast data analysis. These preprocessed files are also
stored in the Apache Parquet format. Furthermore, a redundancy system mirrors
these files on the CBPF and Unicamp clusters, making them accessible for data
analysis to all $\nu$-Angra collaborators. A diagram of the data acquisition
software setup is illustrated in Figure 7.

Figure 7:  $\nu$-Angra Data Acquisition System diagram.

##  4 Analysis

Our analysis pipeline comprises the following sequence of data handling: data
quality assessment and preprocessing, energy calibration, event selection, and
statistical analysis of reactor on/off cycles. In the following subsections,
we provide descriptions of each one of these steps.

###  4.1 Data quality and pre-processing

The data quality assessment and pre-processing phase has three main
objectives:

  * •

To verify that the detector’s physical operating conditions, including
temperature and high voltage, remain within permissible limits and thus ensure
the stability of the detector’s performance;

  * •

To quantify critical low-level physical parameters, such as PMT baselines,
electronic noise, and the charge and timing of pulses.

  * •

To calibrate the essential physical measurements, including the tuning of PMTs
gain at the single photoelectron level.

The fine adjustment of the PMT gains is achieved by aligning the peaks of
single photoelectrons (SPEs). These SPEs originate from thermal noise and are
identified via spectra in the very low dynamic range of our ADCs. The
calibration of the NUDAQ ADCs resulted in the following relationship between
the number of photoelectrons and charge:

$1\text{ pe}=78.0\pm 0.6\text{ DUQs}.$ (1)

By individually adjusting the high voltage of each channel, it is possible to
equalize the gains of the PMTs by alignment of all the SPE peaks around the
same ADC value. The gain uniformity of the PMTs is shown in Figure 8, which
shows the charge distribution of PMT pulses at SPE levels, measured in Digital
Units of Charge (DUQ).

Figure 8: PMT gain equalization at the single photoelectron level. Each
histogram shows the SPE spectrum of a single PMT.

The stability of the detector is crucial for the kind of analysis we are
performing. Figure 9 summarizes different indicators of detector stability for
each three-day run during the second half of the 2020 data acquisition
campaign, which corresponds to the data set examined in this study.

The upper left panel of Figure 9 shows the trigger rate of the detector, as
the first indicator of stability over the analyzed time frame. Another method
of confirming the detector stability is through the analysis of Michel
electrons (ME) from muon decays inside the target. Because this process is
independent of the reactor operating state, the results of the ME analysis are
a reliable and unbiased way to check the detector’s sanity and stability. The
ME selection criteria are described in detail elsewhere [18, 15]. To extract
stability parameters from ME we use Gaussian fits on the ME spectra222A
representative example of the shape of ME spectrum, as measured by the
$\nu$-Angra detector, can be viewed in [15].. The upper right panel
illustrates the consistent stability of the ME counting rate, while the lower
left panel shows a similar trend for the ME peak amplitude, which is directly
proportional to the ME rate. Moreover, the robustness of the energy
measurements can be inferred from the plot in the lower right panel of Figure
9. This plot shows the position of the peak obtained from Gaussian fits over
the ME spectra throughout the period of data analysis. The small fluctuations
in the peak position imply stable energy measurements of the detector during
this period. All of these results add confidence and reliability to our data
analysis.

From the ME analysis, we also cross-checked our veto system efficiency. On
average, the top veto tank has an efficiency of approximately 99.76% [22]. The
average ME rate obtained in our analysis is 0.02 Hz, representing 0.01% of
events in the total trigger rate, which falls within the 0.24% of unvetoed
events. This is a very consistent result, as not all unvetoed muons crossing
the detector have energies in the range to be stopped inside the target.

Figure 9: Upper left: Trigger rate of the $\nu$-Angra detector for the
analyzed dataset; Upper right: Time evolution of Michel electrons (ME)
counting; Lower left: Time evolution of ME spectrum amplitude; Lower right:
Time evolution of ME spectrum peak position.

###  4.2 Calibration and event selection

####  4.2.1 Calibration

To establish the relationship between collected charge and energy, our
collaboration developed a comprehensive and detailed simulation of the
detector using a GEANT4 code. Figure 10 shows a cross-sectional view of the
detector volumes (including the concrete wall of the reactor dome) of the
simulation. The simulation also helps to determine the cut-off criteria for
filtering background noise from the events recorded by the $\nu$-Angra
detector.

Figure 10:  Cross-sectional view of the detector elements generated by our
GEANT4 simulation code.

The energy calibration of our detector must be based on simulations due to
stringent safety restrictions that prohibit the use and handling of
radioactive sources within the nuclear power plant. To elucidate the
correlation between energy and photoelectron (PE) yield, we conducted
simulations of positrons at discrete energy values ranging from 1 MeV to 10
MeV, encompassing the energy range expected of positrons from IBD reactions
induced by neutrinos from the reactor. For each specified energy value, 1200
positrons were fired with uniformly distributed random spatial positions and
isotropic momentum vectors within the target volume of the detector. For each
positron energy E, the PE distribution obtained from the simulation was fitted
with a Gaussian, establishing the correlation between the positron energy E
and the total number of PEs generated in the PMTs (the Gaussian peaks). Figure
11 illustrates the results clearly showing a linear relationship. From the
linear fit, the conversion from the positron energy to the photoelectron count
$N_{pe}$ is given by [23]:

$N_{pe}(E)=26.52\cdot E-16.55$ (2)

Figure 11:  Calibration curve fit to obtain the photoelectron number $N_{pe}$
to energy conversion [23]. Data are the Gaussian peaks from PE distributions
obtained from simulations for each positron energy E. Error bars are the
corresponding Gaussian widths.

Finally, one can convert the measured charge of the detector signals (in DUQ
units) to energy (in MeV) by combining equations 1 and 2.

####  4.2.2 Event selection

The IBD antineutrino interaction can be characterized by three properties
related to the detector signals: prompt and delay energy (see section 2), and
the time interval between two signals.

To select events associated with IBD candidates, we rely on the energy
calibration discussed above. The time interval is the only property not
derived from the simulation; instead, it is determined directly from the
$\nu$-Angra detector data, taking into account the Poissonian nature of the
time between two events.

##### Event time interval:

Figure 12 displays the distribution of the time intervals between the events
measured during the detector commissioning run [15]. From this distribution,
namely a superposition of three Poissonian exponentials ($\propto
exp(-t/\lambda)$), we have identified different physics processes: muon decay,
neutron capture, and background noise. Table 1 lists the time constant
$\lambda$ for each process, obtained from a fit of the data.

Figure 12:  Time interval distribution obtained during the commissioning run of the $\nu$-Angra detector. The data (black histogram) was fitted with a sum of three components: muon decays, neutron captures and background. Red line is the overall fit, dotted lines are the fit results for each individual component.  Component |  $\lambda$
---|---
Muon Decay |  (1.9 $\pm$ 3) $\mu$s
Neutron Capture |  (12.32 $\pm$ 0.05) $\mu$s
Background Noise |  (7.92 $\pm$ 0.01) ms
Table 1: Time constants for each component of the time interval distribution.

With muon decay occurring at $\sim$ 2 $\mu$s and neutron capture at $\sim$ 12
$\mu$s, we define a 4$\lambda$ range limit for these characteristic times.
Consequently, we set the lower limit at 8 $\mu$s and the upper limit at 50
$\mu$s in the time between two events as the initial criterion for event
selection. Thus, the first and second events selected by this criterion define
a pair (prompt,delay).

##### Prompt energy:

The prompt energy should take into account the following expression

$E_{e+}=\sqrt{(E_{\bar{\nu}}-1.3)^{2}-(0.511)^{2}}\leavevmode\nobreak\
\leavevmode\nobreak\ \leavevmode\nobreak\ MeV$ (3)

where $E_{e+}$ is the positron energy and $E_{\bar{\nu}}$ is the electron
antineutrino energy from IBDs. Here, 1.3 MeV represents the mass difference
between a proton and a neutron, and 0.511 MeV is the rest mass of a positron.

The energy cut-off points for prompt events was determined from simulations of
the detector response for IBD positrons. Antineutrino energies are drawn from
a PDF given by the normalized convolution of the spectrum of $\beta$-decays in
the reactor’s fuel the ($\bar{\nu_{e}}$,p) cross-section, then converted to
positron energy using Equation 3. These energies are then assigned to
positrons with uniformly distributed positions and isotropic momenta within
the target volume. The resulting antineutrino and positron spectra used to
simulate IBD prompt events in the detector are shown in Figure 13.

Figure 13:  Positron drawn energy from reactor antineutrino spectrum.  Figure
14: Detector response for IBD positrons generated from reactor antineutrino
spectrum (prompt signal PDF).

The IBD positrons generate Cherenkov photons that are collected by the
detector’s PMTs, resulting in the normalized distribution shown in Figure 14.
This simulation followed the minimum trigger requirement, which is the
activation of a minimum of five PMTs. However, the lower-energy region is
heavily influenced by background noise from sources such as gammas from cosmic
rays and external neutrons. To filter out these background events and tag the
primary event of the pair, we only accept events with prompt energies $E_{p}$
in the range of 3 MeV $\leq E_{p}\leq$ 10 MeV.

##### Delay energy:

In addition to the filtering based on time interval and prompt energy, the
selection criterion for the event pair includes the delayed signal. This third
cut-off criterion was established by simulating neutrons with an energy of 10
keV, with their momentum and position uniformly randomized within the target.
The selection of low neutron energy is a natural outcome of IBD kinematics and
is also essential to achieve rapid thermalization in the simulation, ensuring
neutron capture by gadolinium. Neutron capture is followed by the emission of
deexcitation gammas $\gamma_{Gd}$ that generate Cherenkov radiation through
Compton scattering with electrons in the medium. The spectrum of gammas of the
reaction n(Gd, Gd∗)$\gamma$ is shown in the histogram of Figure 15, and after
normalization we have the PDF of Figure 16, with probabilities in terms of the
number of PE generated in the detector PMTs.

Figure 15: Expected spectrum of the delay signals, produced by gammas from the
de-excitation of Gd after neutron capture [23]. Figure 16: Detector response
for the gammas from de-excitation of Gd atoms in the target after neutron
capture (delay signal PDF).

Using the mean energy and RMS of the $\gamma_{Gd}$ expected spectrum (delay
signals) we can set the lower and upper energy limits for the delay energy Ed
cutoff as 1.60 MeV $\leq E_{d}\leq$ 7.12 MeV.

Another additional criterion derived from the simulations is the PMT
multiplicity in each event. The simulation results analysis revealed that the
$\gamma_{Gd}$ of approximately 8 MeV activate more than 25 PMTs. Signals with
fewer than 25 PMT were generally due to hydrogen capture $n+p\rightarrow
D+\gamma$, which releases typically a gamma with energy of 2.2 MeV after
deuteron deexcitation, as illustrated in Figure 17.

Figure 17: Simulated spectra of gammas from neutron capture filtered by the
multiplicity M of PMTs activated in the events. M$\geq$25 selects efficiently
gammas Gd de-excitation from those of deuteron formation.

###  4.3 ON-OFF analysis

The most straightforward way to check the detector ability to monitor reactor
activity is the ON-OFF analysis. In this analysis, reactor neutrino IBD events
are identified by comparing datasets collected when the reactor is operational
(ON) and during reactor shutdowns for maintenance and refueling (OFF). During
the ON periods, the reactor produces a flux of antineutrinos, absent during
the OFF periods, which serves as a background reference. By comparing the
event rates and characteristics between the ON and OFF datasets, we can
isolate and confirm the presence of reactor antineutrino IBD events,
effectively distinguishing them from background noise and other sources of
interference.

The current analysis was performed over 3 data sets around the reactor
shutdown in 2020. The reactor operation periods are from 08/20/2020 to
09/13/2020, and from 09/24/2020 to 09/28/2020, totalizing a first block of 30
ON days (ON1), and a second one from 10/01/2020 to 10/30/2020 also with 30 ON-
days (ON2). These two blocks of ON data were compared to a non-operational
period from 06/24/2020 to 07/23/2020 (30 OFF days). We applied the event
selection criteria outlined in Section 4.2.2 on these data sets to select
pairs of delayed events corresponding to candidates for IBD. The prompt
spectra of both the ON and OFF periods were obtained and then subtracted from
each other.

The results of the ON1 data set against the OFF period are shown in Figure 18.

Figure 18:  ON1 and OFF prompt spectra (upper panel). The ON1-OFF subtraction,
shows a clear excess of the reactor-ON period (lower panel).

An observable excess in the 4.5 - 6.5 MeV range is completely compatible with
the expected energy spectrum of antineutrino events from IBD. A $\chi^{2}$
hypothesis test was conducted assuming as null hypothesis $H_{0}$ the OFF
spectrum, implying that any excess observed could be due to statistical
background fluctuations.

In our analysis, the null hypothesis $H_{0}$ was decisively rejected by
$\chi^{2}/dof\sim 13.4$, which implies an almost zero p-value. The same test
was applied to the subsequent 30-day ON2 period, with results shown in Figure
19. In this case, $\chi^{2}/dof\sim 17.3$ further reinforced the rejection of
$H_{0}$, thus confirming the previous ON1-OFF results as a genuine excess.

Figure 19:  ON2 and OFF prompt spectra (upper panel). The ON2-OFF subtraction,
shows a clear excess of the reactor-ON period (lower panel)

##  5 Conclusions

The $\nu$-Angra experiment aims to demonstrate significant advancements in the
field of neutrino detection technology through the deployment of a water
Cherenkov detector at the Angra dos Reis nuclear power plant. This detector
may be capable of monitoring antineutrino emissions from nuclear reactors,
thus enhancing nuclear safety measures.

We successfully implemented a sophisticated data acquisition and analysis
system for the $\nu$-Angra experiment. This system played a crucial role in
guaranteeing the quality of the data taken from 2018 to 2022.

Our calibration processes, using experimental data and simulations, were
crucial in refining the detector’s sensitivity to prompt and delayed neutrino
signals.

The application of GEANT4 simulation to define the selection criteria for the
IBD candidates has enabled us to accurately differentiate between neutrino
interactions and background noise.

The ON/OFF analysis conducted during different operational phases of the
reactor provided hints of the detector’s capability to distinguish between
reactor operational states, which is pivotal for reactor monitoring and
safeguards. We clearly observed the excess of IBD candidates from the reactor-
ON periods of the 2020 detector runs. The statistical significance of the
excess is remarkable. However, recognition as genuine IBD events depends on a
deeper understanding of the background. Future efforts will aim to enhance our
event selection criteria by thoroughly examining the background noise, which
is essential for accurate identification of IBD events. Our results
demonstrate the potential of the $\nu$-Angra detector technology for non-
proliferation purposes. We should highlight that we encountered the difficult
task of operating a water Cherenkov neutrino detector at surface level.
Nevertheless we achieved significant background suppression by integrating the
detector’s hardware capabilities with suitable data analysis techniques,
aligning with the International Atomic Energy Agency’s strategies to enhance
nuclear safeguards tools. The ability to monitor reactors in near-field
scenarios with different technologies enhances transparency and compliance
with international nuclear nonproliferation treaties.

##### Acknowledgements

We express our heartfelt gratitude to the late Dr. Ademarlaudo F. Barbosa,
without whom this experiment would never have been possible. We hope we have
made you proud.

## References

  * [1] C. L. Cowan, F. Reines, F. B. Harrison, H. W. Kruse, and A. D. McGuire.  Detection of the free neutrino: A Confirmation.  Science, 124:103–104, 1956.
  * [2] K. Eguchi et al.  First results from kamland: Evidence for reactor anti-neutrino disappearance.  Phys. Rev. Lett. 90 (2003) 021802.
  * [3] Y. Abe et al.  Indication of reactor electron antineutrino disappearance in the double chooz experiment.  Phys. Rev. Lett. 108 (2012) 131801.
  * [4] F. P. An et al.  Observation of electron-antineutrino disappearance at daya bay.  Phys. Rev. Lett. 108 (2012) 171803.
  * [5] J. K. Ahn et al.  Observation of reactor electron antineutrinos disappearance in the reno experiment.  Phys. Rev. Lett. 108 (2012) 191802.
  * [6] L. Mikaelian.  Neutrino laboratory in the atomic plant.  Proceedings of the International Conference ”Neutrino 77” 2 (1978) 383.
  * [7] A. Borovoi and L. Mikaelian.  Possibilities of practical applications of neutrinos.  At. Energ, 44, 6 (1978) 508.
  * [8] V. Korovkin, A. Kodanev, D. Yarichin, et al.  Measurement of nuclear fuel burnup in a reactor according to neutrino emmission.  At. E´nerg, 56, 4 (1984) 214.
  * [9] V. Korovkin, A. Kodanev, Panashchenko, et al.  Measurement of power generation of a power reactor by the method of neutrino detection.  At. E´nerg, 65, 3 (1988) 169.
  * [10] Adam Bernstein, Nathaniel Bowden, Bethany L. Goldblum, Patrick Huber, Igor Jovanovic, and John Mattingly.  $Colloquium$: Neutrino detectors as tools for nuclear security.  Rev. Mod. Phys., 92:011003, 2020.
  * [11] R. Carr et al.  Neutrino-based tools for nuclear verification and diplomacy in north korea.  arXiv:1811.04737 (2018).
  * [12] A. Aguillar-Arevalo et al.  Results of the engineering run of the coherent neutrino nucleus interaction experiment (connie).  JINST 11 (2016) P07024.
  * [13] P. Huber.  Determination of antineutrino spectra from nuclear reactors.  Physical Review C. 84 (2011) 024617.
  * [14] J. C. Anjos, A. Bernstein, M. Cribier, et al.  nternational atomic energy agency - safeguards.  Technical Report No. 361. 2009.
  * [15] H. P. Lima et al.  Neutrinos Angra experiment: commissioning and first operational measurements.  JINST, 14(06):P06010, 2019.
  * [16] Aguilar-Arevalo, Alexis, et al.  Exploring low-energy neutrino physics with the Coherent Neutrino Nucleus Interaction Experiment.  Phys. Rev. D, 100(9):092005, 2019.
  * [17] Hagiwara, Kaito, et al.  Gamma Ray Spectrum from Thermal Neutron Capture on Gadolinium-157.  PTEP, 2019(2):023D01, 2019.
  * [18] L.F.G. Gonzalez.  Estudo da relação sinal / ruído em detectores de antineutrinos de reator.  PhD thesis, IFGW/Unicamp, 2019.
  * [19] T. I. Dornelas, F. T. H. Araújo, A. S. Cerqueira, J. A. Costa, and R. A. Nóbrega.  Front-end Design and Characterization for the $\nu$-Angra Nuclear Reactor Monitoring Detector.  JINST, 11:07018, 2016.
  * [20] H. P. Lima Jr, L. M. de Andrade Filho, L. F. G. Gomez, and R. G. Gama.  Data acquisition with optimal pulse amplitude estimation for a neutrino detection experiment.  Notas Tecnicas CBPF-NT-006/14 (2014).
  * [21] J. M. S. Souza.  Sistema digital de selecão online de eventos para o detector Neutrinos-Angra.  PhD thesis, Universidade Federal da Bahia, 2016.
  * [22] D. M. Souza et al.  Development and characterization of a water-based muon veto for the v-Angra Experiment.  Nucl. Instrum. Meth. A, 1004:165378, 2021.
  * [23] W.V. Santos.  Estudo da Eficiência do Detector Neutrinos-Angra utilizando GEANT4.  PhD thesis, IFGW/Unicamp, 2023.