Original: Reproducibility report for "On Disentangling Spoof Trace for Generic Face Anti-Spooﬁng"
Revised: Reproducibility report for "On Disentangling Spoof Trace for Generic Face Anti-Spooﬁng"

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress email
Revised: AfﬁliationAddress email

Original: Reproducibility SummaryScope of Reproducibility
Revised: Reproducibility SummaryScope of Reproducibility

Original: From previous research(1) it has been demonstrated that face anti-spooﬁng problem can be treated as a denoising problem where spoof images can be disentangled into two parts – live counterpart and the noise.
Revised: From previous research(1), it has been demonstrated that face anti-spooﬁng problem can be treated as a denoisingproblem where spoof images can be disentangled into two parts – live counterpart and the noise.

Original: Based on that, this paper suggests that the disentanglement will be more appropriate if done in four parts - s, b, C, T where - s, b representcolor range bias and balance bias, C denotes the smooth content patterns and, T is the high-level texture patterns.
Revised: Based on that, thispaper(2) suggests that the disentanglement will be more appropriate if done in four parts - s, b, C, T where - s, brepresent color range bias and balance bias, C denotes the smooth content patterns and, T is the high-level texturepatterns.

Original: They claim that by leveraging these features, one can ﬁnd the live counterpart of any image as well as the spoof counterpart of the live inputs by warping
Revised: They claim that by leveraging these features, one can ﬁnd the live counterpart of any image as well as thespoof counterpart of the live inputs by warping.

Revised: We identify the main scope of reproducibility is to verify whether it ispossible to generate the live counterpart from any image by subtracting the spoof trace.

Original:  Methodology
Revised:  Methodology

Original: We approached this challenge in the following manner.
Revised: We approach this challenge in the following manner.

Original: Firstly, we attempt to reproduce the paper in PyTorch whiletaking help from the paper, the authors, and the ofﬁcial implementation.
Revised: Firstly, we have reproduced the paper in PyTorch while takinghelp from the paper, the authors, and the ofﬁcial implementation.

Original: This process took us around a month.
Revised: This process took us around a month.

Original: Secondly,we veriﬁed different segments of the  implementation(e.g., warping function) by using an input of the same value forboth in  our and their implementation.
Revised: Secondly,we checked the soundness of our PyTorch implementation by taking same input in both our and their implementation.

Original: Thirdly, we train on MSU SiW Protocol-1 and OULU NPU Protocol-1 to verifywhether we have succeeded to reproduce their results.
Revised: Later, we train on MSU SiW Protocol-1 and OULU NPU Protocol-1 to verify whether we have succeeded to reproducetheir results in PyTorch.

Original: Each epoch on a single V100 took around 12 hours.
Revised: Each epoch on a single V100 took around 12 hours.

Original: Later, wewent on to propose several improvements over a few identiﬁed limitations of the original paper.
Revised: Finally, we went on to propose severalimprovements over a few identiﬁed limitations of the original paper.

Original:  Results
Revised:  Results

Original: We succeeded to match the ACER of the original paper to within 0.53% on both MSU SiW Protocol-1 and OULU NPU 
Revised: While verifying the reproducibility, we outperformed the result of OULU NPU Protocol-1 as we got ACER of 1 .

Original: Protocol-1.
Revised: 195%compared to their 1 . 9% .

Original: In the original paper, the ACER ofMSU-SiW Protocol is 0% and for us, it is 0 .
Revised: For MSU-SiW Protocol-1, we achieved ACER of 0 .

Original: 53% and the ACER of 
Revised: 53% while they found ACER of 0% .

Original: OULU NPU Protocol-1 is 1 . 9% and for us, it is 1 . Later, we moved our focus to generated images instead of themetrics due to a lack of computation resources. We, while following their implementation, fail to generate perfectlywarped spoof images in many cases and detects unnecessary face details.
Revised: Later,we veriﬁed whether it is possible to get perfect live counterpart from any input image.

Original: We concluded that it’s not always possible toget accurate spoof images.
Revised: We concluded that it is notalways possible to get perfectly warped spoof images in many cases.

Original: Later, we proposed a few techniques to 
Revised: Later, we proposed a few techniques to improvethe generation of the live counterpart based on our observation.

Original: What was easy
Revised: What was easy

Original: As the paper was detailed and the ofﬁcial implementation wasvery easy to read and run, we were at ease whileimplementing the architecture of the paper in PyTorch, despite this being a complex one.
Revised: Although the method was really complicated, as the ofﬁcial implementation was easy to follow, we were at ease whilere-implementing the architecture of the paper in PyTorch.

Original: Besides, we got help from theauthors when we were perplexed, which made things easier.

Original: What was difﬁcult
Revised: What was difﬁcult

Original: The hyperparameters for producing the metrics are not given and said to be selected using the brute force method whichmade it really hard to reproduce.
Revised: The hyperparameters for producing the metrics were not given and, as mentioned in the paper, the authors selected themby brute-forcing. We found it really time-consuming to ﬁnd the best result as the method is prone to hyperparameters.

Original: Communication with original authors
Revised: Communication with original authors

Original: After we read the paper and started implementing the code, we had a few confusions for which we mailed the authorand got help from them.
Revised: After we read the paper and started implementing the code, we had a few confusions for which we mailed the authorand got help from them.

Original: Later, we suggested an inconsistency between the paper and the implementation.
Revised: Later, we suggested an inconsistency between the paper and the implementation.

Original: The following section formatting is optional, you can also deﬁne sections as you deem ﬁt.

Original:  Introduction
Revised:  Introduction

Original: In recent years, the tendency to depend on biometric authentication is increasing.
Revised: In recent years, the tendency to depend on biometric authentication is increasing.

Original: Of all bio-metric authenticationtechnologies, face recognition is  the most intuitive and effective.
Revised: Of all bio-metric authenticationtechnologies, face recognition is one of the most popular technologies.

Revised: One of the main reasons for the widespreaduse of this system is its non-intrusive nature of acquisition and ease of use(3).

Original: With the widespread use of face recognition brings intoquestion the security of face-authentication systems and its robustness against various forms of malicious attacks.
Revised: However, with the extensive use of facerecognition brings into the question of the security of face-authentication systems and its robustness against variousforms of malicious attacks.

Original: Sadly,these systems are susceptible to various forms of presentation attacks (PA), or more commonly known as spooﬁng.
Revised: Sadly, these systems are susceptible to various forms of presentation attacks (PA), or morecommonly known as spooﬁng.

Original: Mereprintouts or recorded videos of faces presented in front of the biometric sensor are enough to fool many face-recognitionsystems.
Revised: Mere printouts or recorded videos of faces presented in front of the biometric sensor areenough to fool many face-recognition systems.

Original: In the past, hand-crafted features like HOG and LBP were used to tackle the problem of face anti-spooﬁng.
Revised: In the past, hand-crafted features(4; 5; 6; 7; 8) like HOG and LBP were used to tackle the problem of face anti-spooﬁng.

Revised: However, with the revolution of high quality cameras, these techniques have become ineffective.

Original: In recentyears, CNNs have been adopted as the preferred solution for this problem.
Revised: In recent years, CNNs have been adopted(9; 10; 11; 12) as the preferred solution for this problem.

Original: CNNs, with techniques like depth supervisionor pixel-wise binary supervision, has shown great potential.
Revised: CNNs, with techniques like depthsupervision or pixel-wise binary supervision(10; 13), have shown great potential.

Original: The paper(2) we reproduced proposes a Generative Adversarial Network based technique to ﬁnd spoof traces from an image.
Revised: The paper(2) we reproduced proposesa Generative Adversarial Network based technique to ﬁnd spoof traces from an image.

Revised: It is based upon the assumptionthat, spoof attack is an additive noise of the live face which is shown behind the spoof attack.

Original: This technique has shown great promise asthe proposed method achieves state of the art (SOTA) performance in both inter and intra dataset testing across differentresearch datasets.
Revised: This technique has showngreat promise as the proposed method achieves state of the art (SOTA) performance in both inter and intra datasettesting across different research datasets.

Original:  Scope of reproducibility
Revised:  Scope of reproducibility

Original: Previously, it has been seen that, face anti spooﬁng problem can be treated as a denoising problem.
Revised: Previously from (1), it has been seen that, face anti spooﬁng problem can be treated as a de-noising problem.

Original: Based on thisﬁnding, the paper we reproduced has extrapolated the following claims:
Revised: Based onthis ﬁnding, the paper we reproduced has extrapolated the following claims:

Original: • Claims that the equation-1 will produce live counterpart of any image.
Revised: • Claims that the equation-2 will produce live counterpart of any image where, I is the input image, and s, b, C, T of 1 are the spoof trace components. Here, the right hand side of equation-2 forms the spoof trace of the inputimage, I . I spoof _ trace should be zero if the input is a live input.

Revised: Finally, is expected to be the live counterpart

Original:  • Treats several cues as irrelevant and claims that it hinders the generalization.
Revised: of the input image I . • Treats several cues as irrelevant and claims that it hinders the generalization.

Original: • Claims that hard and rare(e
Revised: • Claims that hard and rare(e

Original: g., 3D face data) samples can be created using the spoof trace.
Revised: g., 3D face data) samples can be created using the spoof trace.

Original: [Equation]
Revised: [Equation]

Original:  Methodology
Revised:  Methodology

Original: Based on the claims from Section-2, we emphasize on the following methods to verify their claim and then go on topropose ways to tackle few problems that we found in their paper.
Revised: Based on the claims from Section-2, we took the following approaches to verify their claim and then go on to proposeways to tackle few problems that we found in their paper.

Original: • Equation-1 was proposed to get live counterpart of any given input.
Revised: • Equation-1 is used to get the spoof trace of any input image.

Original: Later on, this image is subtracted from the 
Revised: Later on, equation-2 was proposed to get the live

Original: input image to get the ﬁnal spoof trace.
Revised: counterpart of any given input.

Original: We follow the equation-1 and verify whether this claim is true or not. 
Revised: We follow the equations and verify whether this claim is true or not. Our result can be seen in ﬁgure-5.

Original:  • They claim that spoof trace helps to remove the outside cues which effect the generalization ability of the model.
Revised: [Figure] • They claim that spoof trace helps to remove the outside cues which effect the generalization ability of themodel.

Original: We use Fast Fourier Transformation based features as outside cues to verify the claim.
Revised: We use Fast Fourier Transformation based features as outside cues to verify the claim.

Revised: We haveattached ﬁgure-4 to show the outcome of our experiment.

Original: • Finally, while demystifying the above claims, we observed a limitation which is  the reconstructed livecounterpart of spoof images take up a different colored hue.
Revised: • Finally, while demystifying the above claims , we observed a limitation which is – the reconstructed live counterpart of spoof images take up a different colored hue which can be seen in ﬁgure-

Original: We explored a few ways to solve this problem.
Revised: We explored two ways to solve this problem.

Revised: The results of the approaches can be seen in [6,

Original: 3.1 Experimental setup
Revised: 3.1 Experimental setup3.1.1 Architecture

Revised: While implementing the research, we followed the architecture they shared in the paper.

Revised: However, we changed fewsmall changes after discussing with the authors.

Revised: Few things that are to be noted – the batch size should always beeven numbered as we are required to take equal number of spoof and live input images for each batch.

Revised: One otherthing that we were informed about was that they used PReLU instead of LeakyReLU afterwards as they found betterresults with the former.

Revised: Besides, input sizes for the multi-scale discriminator are also changed from (256 , 128 , 64) to(256 , 160 , 40) .

Original: 3.1.1 Datasets
Revised: Because, according to the authors, the later one gave better results. Apart from these changes, we kept every other thing similar to the ofﬁcial implementation. We sent equal number ofspoof and live images into the generator – which outputs the probabilities of early spoof regression and the disentangledspoof traces s, b, C, T . Once we ﬁnd the disentangled values, we create the spoof trace using Equation-1 and then, weget the live counterparts of the inputs using Equation-2 which we call Reconstructed Live image.

Revised: Later, we warp thespoof trace using (14).

Revised: We use the warped spoof trace to create spoof counterpart of the live inputs.

Revised: Finally, we send allthe actual inputs and their synthetic counterparts to the discriminator.

Revised: From Figure-1, the whole process of the networkcan be seen.

Revised: In ﬁgure-3, the network architecture is shown wheareas, the details of the common blocks of the architecture can beseen in ﬁgure-2.

Original: We used protocol-‘ from MSU-SiW and OULU-NPU to verify our claims.
Revised: We used protocol-1 from MSU-SiW and OULU-NPU to verify our claims.

Original: Below, the datasets are discussed:
Revised: Below, the datasets are discussed:

Original: -NPU
Revised: OULU-NPU

Original: OULU-NPU (3) is one of the most recent datasets in the face anti-spooﬁng dataset family.
Revised: OULU-NPU (15) is one of the most recent datasets in the face anti-spooﬁng dataset family.

Original: It consists of 55 subjects andall the recordings were done using six phones and in three different environments (session).
Revised: It consists of 55 subjectsand all the recordings were done using six phones and in three different environments (session).

Original: The session was introducedto better simulate real world situations.
Revised: The session wasintroduced to better simulate real world situations.

Original: To enforce generalization in the data, they followed "leave one out" approachwhere for training, ﬁve of six mobile phones are taken and the remaining one is used for testing.
Revised: To enforce generalization in the data, they followed "leave one out"approach where for training, ﬁve of six mobile phones are taken and the remaining one is used for testing.

Original: There were 3960spoof videos (print and video replay combined) and 1980 bona ﬁde videos.
Revised: There were3960 spoof videos (print and video replay combined) and 1980 bona ﬁde videos.

Original: This makes it by far the most variant anddeﬁnitely larger than MSU-MFSD (4) dataset.
Revised: This makes it by far the most variant and deﬁnitely larger than MSU-MFSD (16) dataset.

Original: Also, there were 4 different protocols.
Revised: Also, there were 4 different protocols.

Original: The protocol names were setbased on the level of difﬁculty.
Revised: The protocol names wereset based on the level of difﬁculty.

Original: Following points show a brief description of OULU-NPU (3) protocols -
Revised: Following points show a brief description of OULU-NPU (15) protocols -

Original: • Protocol I consists of a Test set which has different environments (session) than Dev and Train set.• Protocol II ensures difference in instruments (smartphones) in the Test set as opposed to the Dev and Train set.
Revised: • Protocol I consists of a Test set which has different environments (session) than Dev and Train set. • Protocol II ensures difference in instruments (smartphones) in the Test set as opposed to the Dev and Train set.

Original: • Protocol III uses recording from different phones for Train and Test set.
Revised: • Protocol III uses recording from different phones for Train and Test set.

Original: This helps to verify generalizationcapability of any network.
Revised: This helps to verify generalization capability of any network.

Original: [Table] • Protocol IV is the hardest protocol of them all.
Revised: • Protocol IV is the hardest protocol of them all.

Original: It combines every constraints of the previous protocols.
Revised: It combines every constraints of the previous protocols.

Original: Also,with smaller subset of videos for training and evaluation.
Revised: Also,with smaller subset of videos for training and evaluation.

Original: [Table]
Revised: MSU-SiW

Original: The previous datasets before this (5) had lesser subjects compared to this one’s 165 subjects where each subject hasup to 8 bona-ﬁde videos and 20 spoof videos.
Revised: The previous datasets before this (17) had lesser subjects compared to this one’s 165 subjects where each subject hasup to 8 bona-ﬁde videos and 20 spoof videos.

Original: All the videos are captured in 30 frames per second on high deﬁnition
Revised: All the videos are captured in 30 frames per second on high deﬁnition

Original: Cameras – a Canon EOS T6 and a Logitech C920.
Revised: Cameras – a Canon EOS T6 and a Logitech C920.

Original: The dataset provides print and replay attacks based on four sessionsto capture the videos which included pose, illumination, distance and expression.
Revised: The dataset provides print and replay attacks based on four sessionsto capture the videos which included pose, illumination, distance and expression.

Original: Furthermore, to set a baseline forfuture studies, three protocols are created -
Revised: Furthermore, to set a baseline forfuture studies, three protocols are created -

Original: • Protocol I uses the ﬁrst 60 frames of the videos for training and uses all the videos on the testing set.
Revised: • Protocol I uses the ﬁrst 60 frames of the videos for training and uses all the videos on the testing set.

Original: Thiswould test if the model is generalized for contrasting face poses and expressions.
Revised: Thiswould test if the model is generalized for contrasting face poses and expressions.

Original: • Protocol II leverages the leave-one-out strategy to train on three mediums and test on the remaining one, tounderstand if the model is invariant to varying mediums.
Revised: • Protocol II leverages the leave-one-out strategy to train on three mediums and test on the remaining one, tounderstand if the model is invariant to varying mediums.• Protocol III tests the models performance on different forms of attack by training on one print attacks, testingon replay attacks and vice-versa. 3.1.3 Resources We mostly followed the original paper and the github repository provided by the authors to reproduce our version. During the implementation phase, we discovered an inconsistency between the paper and the ofﬁcial implementation inthe synthetic early spoof regressor loss.

Original: • Protocol III tests the models performance on different forms of attack by training on one print attacks, testingon replay attacks and vice-versa.
Revised: We even created a pull request to ﬁx the problem although the authors ﬁxed theproblems on their own afterwards. The details of our pull request can be found here. Besides this, we observed fewother inconsistencies in labels which were resolved by contacting the authors. Moreover, while implementing, we hadimmense support from PyTorch discuss forum.

Original: 3.1.2 Memory Usage
Revised: 3.1.4 Memory Usage

Original: We had access to a single Tesla V100 and a single Tesla K80 for training.
Revised: We had access to a single Tesla V100 and a single Tesla K80 for training.

Original: For veriﬁcation purposes, we used the v100as we required to train using batch size of 8 to be consistent with the original paper.
Revised: For veriﬁcation purposes, we used the v100as we required to train using batch size of 8 to be consistent with the original paper.

Original: For the separate extensions, weperformed all our experiments on a single Tesla K80.
Revised: For the separate extensions, weperformed all our experiments on a single Tesla K80.

Original: 3.1.3 Implementation
Revised: 3.2 Implementation

Original: We implemented most of our codes in PyTorch .
Revised: We implemented most of our codes in PyTorch .

Original: However, there is a functionality which is not available in PyTorch,for that, we adapted a code snipped from TensorFlow to make the code faster in terms ofthat speciﬁc functionality. To do this, we converted the PyTorch tensor to  a TensorFlow tensor for that block and later, converted it back to
Revised: The original implementation was done in tensorflow − 1 . 13 . 0 . As discussed previously, to ﬁt the spoof trace of the input image to another input, one requires warping the inputimage. While warping, the original implementation requires a method of tensorflow named gather _ nd .

Original: PyTorch tensor. Besides, to get the warping matrix of the spoof keypoint to live keypoint, we followed the ofﬁcialimplementation.
Revised: This methodtakes a tensor and the indices(up to n-dimension) representing locations in that tensor.

Original: Furthermore, we compared several code blocks of ours with theirs whenever we had doubts.
Revised: Then, it returns the elementsof the tensor corresponding to the indices provided.

Original: Ourimplementation: https://github.com/gazeai/STDN-PyTorch
Revised: However, till PyTorch − 1 . 6 , this method is not implementedin PyTorch.

Original: 3.1.4 Resources
Revised: Although, we implemented our version of that method in PyTorch, it was making the execution of ourimplementation too slow. So, instead of using our implementation, we adapted the tensorﬂow method in ours. We didthis by converting the subsequent PyTorch tensor to a TensorFlow tensor for that speciﬁc block and later, convertedit back to a PyTorch tensor.

Original: We mostly followed the original paper and the github repository provided by the authors to reproduce ourversion. During the implementation phase, we found inconsistencies between the paper and the ofﬁcial implementation.
Revised: Apart from this, we implemented whole codebase from scratch while only looking intothe ofﬁcial implementation for hyperparameters and architecture details. To verify that our implementation is same astheirs, we sent same input to both implementations.

Original: Thesewere resolved by contacting the authors.
Revised: We found that, the output result matches up to two decimal points.

Original: Besides, while implementing, we had support from PyTorch discuss forum.
Revised: Our implementation can be found here: https://github.com/gazeai/STDN-PyTorch

Original:  Our Contribution
Revised:  Our Contribution

Original: 4.1 Fast Fourier Transformation as Cue
Revised: 4.1 Fast Fourier Transformation as Cue

Original: As per their paper, cues of spoof are too varied in real life scenario and spoof detection as a binary classiﬁcation taskwould take those cues into account to make the ﬁnal decision.
Revised: As per their paper, cues of spoof are too varied in real life scenario and spoof detection as a binary classiﬁcation taskwould take those cues into account to make the ﬁnal decision.

Original: But in reality, those cues might be irrelevant to spoof.
Revised: But in reality, those cues might be irrelevant to spoof.

Original: For example illumination.
Revised: For example illumination.

Original: This hinders the generalization ability of a model.
Revised: This hinders the generalization ability of a model.

Original: Often the illumination and environmentpresent in a research dataset contains much less variations than a real life scenario.
Revised: Often the illumination and environmentpresent in a research dataset contains much less variations than a real life scenario.

Original: To deal with this, they use onlyspoof trace to make the ﬁnal decision.
Revised: To deal with this, they use onlyspoof trace to make the ﬁnal decision.

Original: This has shown to prove good generalization ability as it doesn’t take those spoofcues into account.
Revised: This has shown to prove good generalization ability as it doesn’t take those spoofcues into account.

Original: We however, tried incorporating Fast Fourier Transformation (FFT) into the ﬁnal decision making process.
Revised: We however, tried incorporating Fast Fourier Transformation (FFT) into the ﬁnal decision making process.

Revised: To add FFT,we created a new head in the Encoder block of the generator.

Revised: This head transforms the features using FFT and later,we added the FFT features with the ﬁnal encoder output.

Revised: This effected the early spoof regression features and thus,effecting the ﬁnal prediction.

Original: In ﬁgure-1,we can see the results.
Revised: In ﬁgure-4, we can see the results.

Original: It changes color tone to pink which is also visible in the main implementation of the paper[Figure2].
Revised: It changes color tone to pink which is also visible inthe main implementation of the paper[Figure-5].

Original: However, it performs worse than the main implementation as we can see that, unlike the main implementation, the
Revised: However, it performs worse than the main implementation.

Original:  FFT implementation introduces a checkerboard texture in the generated images.
Revised: Because,the FFT implementation introduces a checkerboard texture in the generated images which is not present in the mainimplementation.

Original: 4.2 MinMax Normalization
Revised: 4.2 MinMax Normalization

Original: In Equation-1 , I is the input image within the range of [0 , 1] and,  s, b, C, T ranging within 1, [-1] according to theofﬁcial implementation of the paper.
Revised: In Equation-2 , I is the input image within the range of [0 , 1] and, from equation-1, s, b, C, T ranging within 1]according to the ofﬁcial implementation of the paper.

Original: If we think of a scenerio where s=b=C
Revised: If we think of a scenerio where s=b=C

Original: =T=-1, the reconstructed image I hat will fallinto the range of [4, 5] and on the other hand, if s=b=C=
Revised: =T=-1, the reconstructedimage I hat will fall into the range of [4, 5] and on the other hand, if s=b=C=

Original: T=1, the range will be 4, -3] which [-will produce unrealistictraces.
Revised: T=1, the range will be willproduce unrealistic traces.

Original: This we can see in Figure-2 where the images add a pink and green hue in the faces and traces.
Revised: This we can see in Figure-5 where the images add a pink and green hue in the faces andtraces.

Original: To solve thisproblem, our initial solution was to normalize the reconstructed image I hat using equation-2 which ranges from [0 , 1] .
Revised: To solve this problem, our initial solution was to normalize the reconstructed image I hat using equation-3 whichranges from [0 , 1] .

Original: [Equation]
Revised: [Equation]

Original: From Figure-3, we can see that the problem of changing hue has been solved, However, as we are normalizing thereconstructed images using minmax normalization, the reconstructed live counterpart of spoof inputs tend to distort asthe model starts to converge. 
Revised: From Figure-6, we can see that the problem of changing hue has been solved, However, as we are normalizing thereconstructed images using minmax normalization, the reconstructed live counterpart of spoof inputs tend to distort asthe model starts to converge. 4.3 Hyperbolic tangent on Spoof-Trace

Original: Although MinMax Normalization solves the problem of unnatural image range partially, another problem arises whichis – if we normalize the reconstructed images within [0 , 1] range, we might fail to capture the traces where spoofimages tend to be darker than live images.
Revised: Although MinMax Normalization solves the problem of unnatural image range partially, another problem arises whichis – if we normalize the reconstructed images within [0 , 1] range, we might fail to capture the traces where spoofimages tend to be darker than live images.

Original: To solve this problem, we used equation-4 on the the trace we will get fromequation-3.
Revised: To solve this problem, we used equation-5 on the the trace we will get fromequation-4.

Original: This ensures that the trace will always fall within the limit of [ − 1 , 1] and helps to solve the problem of lessbright spoof images.[Equation] I )
Revised: This ensures that the trace will always fall within the limit of [ − 1 , 1] and helps to solve the problem of lessbright spoof images.

Original: [Equation]
Revised: [Equation]

Original: From Figure-4, we can see that this solution succeeds to ﬁnd the spoof trace – which is a striped pattern – as well asmaintains the color of the image as well.
Revised: From Figure-7, we can see that this solution succeeds to ﬁnd the spoof trace – which is a striped pattern – as well asmaintains the color of the image as well.

Original: However, like all other above mentioned technique, this also fails to generateperfect outputs as the live counterpart has some jitter in it. 
Revised: However, like all other above mentioned technique, this also fails to generateperfect outputs as the live counterpart has some jitter in it. trace of

Original:  Spoof Detection Results
Revised:  Spoof Detection Results

Original: From Table-1, it can be seen that, our implementation for MSU-SiW Protocol-1 matches their ACER close to 0 .
Revised: From Table-1, it can be seen that, our implementation for MSU-SiW Protocol-1 matches their ACER close to 0 .

Original: On the other hand, our implementation on OULU NPU Protocol-1 outperforms their results by almost 0 .
Revised: On the other hand, our implementation on OULU NPU Protocol-1 outperforms their results by almost 0 .

Original: 3% whichshows the robustness of our implementation.
Revised: 3% whichshows the robustness of our implementation.

Original: [Figure]
Revised: [Figure]

Revised: Our implementation Protocol

Original: [Table] 
Revised: [Table] [Table] [Table] [Table]

Revised: [Table]

Original:  However, as discussed previously, our implementation based on their ofﬁcial repository demonstrates a discolored hueand fails to generate perfect spoof and live images.
Revised: [Table] [Table] our implementation. However, as discussed previously, our implementation based on their ofﬁcial repository demonstrates a discolored hueand fails to generate perfect spoof and live images.

Original:  Discussion
Revised:  Discussion

Original: During the time of our study of the paper, we have observed many aspects of the paper.
Revised: During the time of our study of the paper, we have observed many aspects of the paper.

Original: Some of these strengthen thepaper and some of these are just mere scopes to improve the paper.
Revised: Some of these strengthen thepaper and some of these are just mere scopes to improve the paper.

Original: In the following subsections, we will discuss them.
Revised: In the following subsections, we will discuss them.

Original: 6.1 What worked
Revised: 6.1 What worked

Original: Firstly, we would like to discuss our observation of the original paper.
Revised: Firstly, we would like to discuss our observation of the original paper.

Original: We found the paper to match the implementationwith the paper a great deal.
Revised: We found the paper to match their implementationalmost in every cases.

Original: The things that did not match are just the improvements that the authors are continuouslyworking hard for.
Revised: The things that did not match are just the improvements that the authors are continuously workinghard for.

Original: We found the claim that – live inputs will produce blackened spoof trace and spoof input willwill highlight the spooftextures – to be true.
Revised: We found the claim that – live inputs will produce blackened spoof trace and spoof input will highlight the spooftextures – to be true.

Original: Also, the claim that outside cues hinder the generalization of face-anti-spooﬁng to be true as well.
Revised: Also, the claim that outside cues hinder the generalization of face-anti-spooﬁng to be true as well.

Original: For this we took Fast Fourier Transformed features as an outside cue and the results were not as effective as the originalone which we showed in Figure-1.
Revised: For this, we took Fast Fourier Transformed features as an outside cue and the results were not as effective as the originalone which we showed in Figure-4.

Original: Secondly, in our extended work, we used tanhfunction on spoof-traces to set the limit within [ − 1 , 1] .
Revised: Secondly, in our extended work, we used tanhfunction on spoof-traces to set the limit within [ − 1 , 1] .

Original: This made thesynthetic spoof images more realistic, images shown in Figure-4.
Revised: This made thesynthetic spoof images more realistic, images shown in Figure-7.

Original: 6.2 What did not work
Revised: 6.2 What did not work

Original: Although, we managed to reproduce close to 0 .
Revised: Although, we managed to reproduce close to 0 .

Original: 53% , for the original paper, after a certain time of training, thereconstructed live and spoof images tend to be of more Pink and green hue.
Revised: 53% , for the original paper, after a certain time of training, thereconstructed live and spoof images tend to be of more Pink and green hue.

Original: We believe, this problem existed because ofthe traces from equation-3 to not being within [ − 1 , 1] range.
Revised: We believe, this problem existed because ofthe traces from equation-4 to not being within [ − 1 , 1] range.

Original: Finally, the problem that we could not solve and seemed to be a big problem is that – whenever there is a side face inone of the inputs, the warping function doesn’t work properly which is shown in Figure-5.
Revised: Finally, the problem that we could not solve and seemed to be a big problem is that – whenever there is a side face inone of the inputs, the warping function doesn’t work properly which is shown in Figure-8.

Original: It creates some form ofstretching mark.
Revised: It creates some form ofstretching mark.

Original: Besides, whenever there are any extraneous object on the face, the spoof trace network considers themas a part of spoof trace.
Revised: Besides, whenever there are any extraneous object on the face, the spoof trace network considers themas a part of spoof trace.

Original: For instance, spectacles or any kind of birth mark in faces [Figure-6].
Revised: For instance, spectacles or any kind of birth mark in faces [Figure-9].

Original: 6.3 What was easy
Revised: 6.3 What was easy

Original: Despite the complicated technique, we found the paper to be understandable because of the diagrams.
Revised: Despite the complicated technique, we found the paper to be understandable because of the diagrams.

Original: Moreover, wewere able to match most of the techniques with code snippets from the ofﬁcial implementation.
Revised: Moreover, wewere able to match most of the techniques with code snippets from the ofﬁcial implementation.

Original: This helped us to get aclear perspective of what the author tried to say.
Revised: This helped us to get aclear perspective of what the author tried to do.

Original: Besides, when we found some mismatches – such as: six channelinput in the code – we contacted the author and they helped us clear the confusion.
Revised: Besides, when we found some mismatches – such as: six channel inputin the code – we contacted the author and they helped us clear the confusion.

Original: Besides, all the hyperparameters for training the technique were given which saved us from brute-forcing the traininghyperparameters.
Revised: Besides, all the hyperparameters for training the technique were given which saved us from brute-forcing the traininghyperparameters.

Original: We advise anyone who wants to re-implement this paper is just to read the code and the paper concurrently and writethings up and draw diagrams to make things easier.
Revised: We advise anyone who wants to re-implement this paper is just to read the code and the paper concurrently and writethings up and draw diagrams to make things easier.

Original: 6.4 What was difﬁcult
Revised: 6.4 What was difﬁcult

Original: To get the metrics, the paper expects us to depend on a lot of hyperparameters.
Revised: To get the metrics, the paper expects us to depend on a lot of hyperparameters.

Original: However, these were not explicitlymentioned anywhere in the paper.
Revised: However, these were not explicitlymentioned anywhere in the paper.

Original: We believe that this part of the paper can be highly improved to make it lesshyperparamer-prone.
Revised: We believe that this part of the paper can be highly improved to make it lesshyperparamer-prone.

Original: However, as a temporary solution, we followed their suggestion and used brute-force method toget to the best solution.
Revised: However, as a temporary solution, we followed their suggestion and used brute-force method toget to the best solution.

Original: Furthermore, the paper is an extremely complicated one to read, not because of its language, but because of thetechnique.
Revised: Furthermore, the paper is an extremely complicated one to read, not because of its language, but because of thetechnique.

Original: The technique demands to have three training steps and each time with different types of requirements.
Revised: The technique demands to have three training steps and each time with different types of requirements.

Original: Itmade things really difﬁcult for us to re-implement it.
Revised: Itmade things really difﬁcult for us to re-implement it.

Original: To make sure of correctness, we divided the steps into smallsections and renamed them in a way that we can identify these sections easily.
Revised: To make sure of correctness, we divided the steps into smallsections and renamed them in a way that we can identify these sections easily.

Original: Later, we veriﬁed these separate codesnippets to ensure the reproduction.
Revised: Later, we veriﬁed these separate codesnippets to ensure the reproduction.

Original: This way we managed to overcome this problem.
Revised: This way we managed to overcome this problem.

Original: 6.5 Future works
Revised: 6.5 Future works

Original: While the original paper introduced us to a briliant approach to tackle face-anti-spooﬁng, there are several scopes whichcan be addressed in future:
Revised: While the original paper introduced us to a briliant approach to tackle face-anti-spooﬁng, there are several scopes whichcan be addressed in future:

Original: • The color and textures are not yet up to the mark as the color becomes less vibrant and the texture tends toshow artifact
Revised: • The color and textures are not yet up to the mark as the color becomes less vibrant and the texture tends toshow artifact

Original: effects.•
Revised: effects.•

Original: The warping for the side angle is a scope to work on.•
Revised: The warping for the side angle is a scope to work on.•

Original: We can use a self attention so that we can ignore any speciﬁc marks on faces.
Revised: We can use a self attention so that we can ignore any speciﬁc marks on faces.

Original: 6.6 Communication with original authors
Revised: 6.6 Communication with original authors

Original: While reading the paper as well as the implementation, we had a few confusions for which we got help from the authors:1.
Revised: While reading the paper as well as the implementation, we had a few confusions for which we got help from the authors:1.

Original: We couldn’t connect the mention of using L2-regularizor in section 3.1 with the implementation.
Revised: We couldn’t connect the mention of using L2-regularizor in section 3.1 with the implementation.

Original: The authorshelped us by pointing out to the line number of the code.2.
Revised: The authorshelped us by pointing out to the line number of the code.

Original: The authors continuously update the code base with improvements.
Revised: The authors continuously update the code base with improvements.

Original: We found two such inconsistencies westarted implementing the code base.
Revised: We found two such inconsistencies westarted implementing the code base.

Original: The author solved our query and later we integrated the improvementsinto our codebase.3.
Revised: The author solved our query and later we integrated the improvementsinto our codebase.

Original: We were unsure how early spoof regressor was workingin the code base.
Revised: We were unsure how early spoof regressor was working

Revised: in the

Revised: code base

Original: The authors ensured us with this aswell.
Revised: The authors ensured us with this as well