Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE

Inês Valentim, Nuno Antunes, Nuno Lourenço·June 20, 2024

Summary

This paper investigates layerwise adversarial robustness in CNNs for image classification, using t-SNE and a custom metric to visualize changes in input representation under attack. The study tests two models (a manually-designed Wide Residual Network and a NeuroEvolution-generated CNN) on CIFAR-10, revealing that vulnerabilities emerge early in feature extraction layers, with a decline in robustness starting from these layers. The metric supports these findings by showing a decrease in robustness as evident in t-SNE maps. The research contributes to understanding adversarial robustness and suggests that future work should focus on designing more robust models, analyzing the impact of learning strategies, and generalizability to different architectures.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of adversarial examples compromising the robustness of Artificial Neural Networks (ANNs) by proposing a method to quantify and visually analyze the discrepancies between the latent representations of clean and adversarial samples in different layers of Convolutional Neural Networks (CNNs) . This problem is not new, as there is an extensive literature showing the vulnerability of both manually-designed ANNs and those designed in an automated manner to adversarial attacks . The proposed approach focuses on exploring weaknesses in ANNs when faced with adversarial examples, emphasizing the importance of understanding these vulnerabilities for defense development .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the robustness of Artificial Neural Networks (ANNs) against adversarial attacks . The study focuses on evaluating the vulnerability of manually-designed ANNs as well as ANNs designed in an automated manner to adversarial attacks . The research seeks to quantify and visually examine the differences in latent representations of clean and adversarial samples across different layers of ANNs, particularly Convolutional Neural Networks (CNNs) used for image classification tasks . The hypothesis revolves around understanding the impact of adversarial perturbations on the inner workings of ANNs and aims to propose a method for visualizing and analyzing changes in the representation of input data as it traverses through various layers of an ANN .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel method to analyze the robustness of Artificial Neural Networks (ANNs) against adversarial examples by quantifying and visually examining the discrepancies between the latent representations of clean and adversarial samples . This method focuses on multi-layer analysis, revealing that discrepancies between clean and perturbed data are present even during feature extraction, before the final convolutional layer . The paper introduces a layerwise robustness metric that aids in defense development, potentially improving fitness functions or selecting layers for detection-based defenses .

Furthermore, the paper suggests evaluating the proposed approach on more datasets and with models explicitly designed to be adversarially robust in the future . The method involves selecting and performing an adversarial attack on correctly classified images, passing both perturbed and clean images through the ANN to extract hidden representations up to the desired target layer, and applying the t-distributed Stochastic Neighbor Embedding (t-SNE) method to visualize the extracted representations in a two-dimensional map . This visualization helps in comparing clean and perturbed image embeddings to measure layer robustness .

The paper also discusses the use of white-box attacks, specifically 𝐿∞ perturbations with 𝜖 = 8/255 and 𝐿2 perturbations with 𝜖 = 0.5, along with the Auto-PGD method for attacks . It highlights that network deterioration begins in the feature extraction layers, impacting how CNNs differentiate between clean and perturbed images, which is reflected in their separation on the t-SNE maps . Additionally, the paper emphasizes the importance of attacking instances from multiple classes to detect overlaps on the t-SNE map, indicating layerwise robustness . The proposed method for analyzing the adversarial robustness of Artificial Neural Networks (ANNs) offers several key characteristics and advantages compared to previous methods outlined in the paper :

  1. Layerwise Analysis: The method focuses on multi-layer analysis, examining the discrepancies between the latent representations of clean and adversarial samples at different layers of Convolutional Neural Networks (CNNs) . This layerwise approach allows for a detailed understanding of how network deterioration begins in the feature extraction layers, impacting the model's ability to differentiate between clean and perturbed images .

  2. Visualization with t-SNE: The method utilizes the t-distributed Stochastic Neighbor Embedding (t-SNE) technique for visual inspection, enabling the quantification of differences between original and altered data as it progresses through the network layers . This visualization aids in measuring layer robustness by comparing clean and perturbed image embeddings, providing insights into the inner workings of the model .

  3. Robustness Metric: A robustness metric is proposed based on the differences between clean and adversarially perturbed representations on the t-SNE map . This metric helps in evaluating the network's robustness by measuring the overlap between clean and perturbed image embeddings, indicating layerwise robustness .

  4. Evaluation on Datasets and Models: The method suggests evaluating the approach on more datasets and with models explicitly designed to be adversarially robust in the future . This approach ensures the generalizability and effectiveness of the proposed method across different datasets and model architectures.

  5. Attack Strategies: The method employs white-box attacks, specifically 𝐿∞ and 𝐿2 perturbations, using the Auto-PGD method for attacks . By considering different attack strategies, the method provides a comprehensive evaluation of the model's robustness against adversarial examples.

  6. Experimental Setup: The experiments are conducted using Python with TensorFlow and PyTorch, utilizing the CIFAR-10 dataset and various model architectures . The method ensures data balance through stratification and includes attacks on correctly classified images for a fair comparison between models.

In conclusion, the proposed method offers a comprehensive and systematic approach to analyzing the adversarial robustness of ANNs, providing insights into network vulnerabilities at different layers and offering a robustness metric for evaluation. By leveraging visualization techniques and robust attack strategies, this method contributes to advancing the understanding and defense development against adversarial attacks in neural networks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of exploring layerwise adversarial robustness. Noteworthy researchers in this area include Aleksander Madry, Alexey Kurakin, Nicholas Carlini, David A. Wagner, Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Zhao, Heather Zheng, Prateek Mittal, Francesco Croce, Maksym Andriushchenko, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Matthias Hein, Chaitanya Devaguptapu, Devansh Agarwal, Gaurav Mittal, Pulkit Gopalani, Vineeth N Balasubramanian, Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy, Inês Valentim, Nuno Lourenço, Nuno Antunes, Laurens van der Maaten, Geoffrey Hinton, Sergey Zagoruyko, Nikos Komodakis, and many others .

The key to the solution mentioned in the paper involves analyzing the different layers of a Convolutional Neural Network (CNN) from an adversarial robustness perspective. The proposed methodology includes selecting and performing adversarial attacks, extracting hidden representations from the ANN, applying the t-distributed Stochastic Neighbor Embedding (t-SNE) method to visualize the representations, and computing a metric that measures the overlap between clean and perturbed image embeddings in the t-SNE space to quantify layer robustness . This approach helps in identifying weak spots in the layers early on, aiding in defense development and potentially improving fitness functions in NeuroEvolution approaches or selecting layers for detection-based defenses .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted using Python 3.8, Tensorflow 2.5.0, and PyTorch 1.10.1 .
  • The CIFAR-10 dataset's test set was split into a validation set and a final test set, each containing 5000 images, with adversarial examples generated for the validation images .
  • White-box attacks were performed with 𝐿∞ perturbations of 𝜖 = 8/255 and 𝐿2 perturbations of 𝜖 = 0.5 .
  • The experiments included using a CNN designed by NE and a handcrafted architecture as a baseline, specifically the WRN-28-10 model trained by the RobustBench team .
  • The methodology involved selecting and performing adversarial attacks on correctly classified images, extracting hidden representations from the ANN up to the desired target layer, and applying the t-SNE method to visualize the representations .
  • The robustness metric proposed in the paper was based on comparing the differences between the clean and adversarially perturbed representations on the t-SNE map .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the CIFAR-10 dataset, which was split into a validation set and a final test set, each containing 5000 images . The code for the experiments conducted in the study is open source and can be accessed through the following links:


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified regarding adversarial robustness in Artificial Neural Networks (ANNs) . The study explores the vulnerabilities of ANNs to adversarial examples, which are crafted to deceive the models by introducing imperceptible perturbations to benign data samples . By conducting experiments using pre-trained models without re-training, the paper investigates the discrepancies between clean and perturbed data representations across different layers of Convolutional Neural Networks (CNNs) .

The research methodology involves analyzing the latent representations of clean and adversarially perturbed images in various layers of the CNNs using the t-distributed Stochastic Neighbor Embedding (t-SNE) technique . This approach allows for a visual examination of the differences between original and altered data, providing insights into the inner workings of the models . The experiments focus on evaluating the robustness of the models against 𝐿2 and 𝐿∞ perturbations, revealing that network deterioration begins in the feature extraction layers, impacting the models' ability to distinguish between clean and perturbed images .

Furthermore, the results demonstrate that the discrepancies between clean and perturbed representations emerge early on in the feature extraction layers of the CNNs, affecting subsequent classification . The visual analysis of the t-SNE maps supports the findings obtained through the proposed robustness metric, which measures the differences between clean and perturbed embeddings to identify weak spots in the layers . Overall, the experiments and results in the paper provide substantial evidence to validate the scientific hypotheses related to adversarial robustness in ANNs and offer valuable insights for defense development and model evaluation .


What are the contributions of this paper?

The contributions of the paper "Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE" include:

  • Proposing a method to quantify and visually examine the discrepancies between the latent representations of clean and adversarial samples in neural networks .
  • Introducing a layerwise robustness metric that aids in defense development and can be used to improve fitness functions in neuroevolution or select layers for detection-based defenses .
  • Conducting experiments on the CIFAR-10 dataset using various models, including a CNN designed by NeuroEvolution, to analyze adversarial robustness .
  • Utilizing white-box attacks with 𝐿∞ and 𝐿2 perturbations to evaluate the robustness of the models .
  • Demonstrating that network deterioration due to adversarial attacks begins in the feature extraction layers, impacting the ability of CNNs to differentiate between clean and perturbed images .

What work can be continued in depth?

Further research in this area can delve deeper into evaluating the robustness of Artificial Neural Networks (ANNs) against adversarial examples. One avenue for future work is to expand the evaluation of the proposed method on additional datasets beyond CIFAR-10 and with models explicitly designed for adversarial robustness . Additionally, exploring the impact of different learning strategies on the adversarial robustness of models could be a valuable direction for further investigation . Furthermore, the study suggests potential applications of the layerwise robustness metric in enhancing NeuroEvolution fitness functions or selecting layers for detection-based defenses, indicating a promising area for future research .


Introduction
Background
Overview of adversarial attacks in image classification
Importance of understanding CNN vulnerability
Objective
To analyze layerwise robustness in CNNs
To propose a custom metric and t-SNE for visualization
To compare manually-designed and NeuroEvolution-generated models
Methodology
Data and Models
CIFAR-10 dataset
Wide Residual Network (WRN)
NeuroEvolution-generated CNN
Data Collection and Attack Strategies
White-box and black-box adversarial attacks
Perturbation methods (e.g., FGSM, PGD)
Feature Representation Analysis
t-SNE Visualization
Pre- and post-attack feature embeddings
Changes in input representation under adversarial attacks
Custom Robustness Metric
Definition and calculation of the metric
Correlation with t-SNE maps
Experiment and Results
Testing models' robustness at different layers
Observations on vulnerability patterns
Findings
Early emergence of vulnerabilities in feature extraction layers
Decline in robustness from these layers
Custom metric's validation of the observed trends
Implications and Future Directions
Model Design
Recommendations for more robust architectures
Role of learning strategies in robustness
Generalizability
Extending the study to different CNN architectures
Cross-validation with diverse datasets
Conclusion
Summary of key insights on adversarial robustness
Importance of the proposed visualization tools
Call to action for further research in the field
Basic info
papers
neural and evolutionary computing
machine learning
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper?
What does the custom metric in the paper reveal about adversarial robustness?
On which dataset are the models evaluated?
Which models are tested in the study?

Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE

Inês Valentim, Nuno Antunes, Nuno Lourenço·June 20, 2024

Summary

This paper investigates layerwise adversarial robustness in CNNs for image classification, using t-SNE and a custom metric to visualize changes in input representation under attack. The study tests two models (a manually-designed Wide Residual Network and a NeuroEvolution-generated CNN) on CIFAR-10, revealing that vulnerabilities emerge early in feature extraction layers, with a decline in robustness starting from these layers. The metric supports these findings by showing a decrease in robustness as evident in t-SNE maps. The research contributes to understanding adversarial robustness and suggests that future work should focus on designing more robust models, analyzing the impact of learning strategies, and generalizability to different architectures.
Mind map
Correlation with t-SNE maps
Definition and calculation of the metric
Changes in input representation under adversarial attacks
Pre- and post-attack feature embeddings
Cross-validation with diverse datasets
Extending the study to different CNN architectures
Role of learning strategies in robustness
Recommendations for more robust architectures
Observations on vulnerability patterns
Testing models' robustness at different layers
Custom Robustness Metric
t-SNE Visualization
Perturbation methods (e.g., FGSM, PGD)
White-box and black-box adversarial attacks
NeuroEvolution-generated CNN
Wide Residual Network (WRN)
CIFAR-10 dataset
To compare manually-designed and NeuroEvolution-generated models
To propose a custom metric and t-SNE for visualization
To analyze layerwise robustness in CNNs
Importance of understanding CNN vulnerability
Overview of adversarial attacks in image classification
Call to action for further research in the field
Importance of the proposed visualization tools
Summary of key insights on adversarial robustness
Generalizability
Model Design
Custom metric's validation of the observed trends
Decline in robustness from these layers
Early emergence of vulnerabilities in feature extraction layers
Experiment and Results
Feature Representation Analysis
Data Collection and Attack Strategies
Data and Models
Objective
Background
Conclusion
Implications and Future Directions
Findings
Methodology
Introduction
Outline
Introduction
Background
Overview of adversarial attacks in image classification
Importance of understanding CNN vulnerability
Objective
To analyze layerwise robustness in CNNs
To propose a custom metric and t-SNE for visualization
To compare manually-designed and NeuroEvolution-generated models
Methodology
Data and Models
CIFAR-10 dataset
Wide Residual Network (WRN)
NeuroEvolution-generated CNN
Data Collection and Attack Strategies
White-box and black-box adversarial attacks
Perturbation methods (e.g., FGSM, PGD)
Feature Representation Analysis
t-SNE Visualization
Pre- and post-attack feature embeddings
Changes in input representation under adversarial attacks
Custom Robustness Metric
Definition and calculation of the metric
Correlation with t-SNE maps
Experiment and Results
Testing models' robustness at different layers
Observations on vulnerability patterns
Findings
Early emergence of vulnerabilities in feature extraction layers
Decline in robustness from these layers
Custom metric's validation of the observed trends
Implications and Future Directions
Model Design
Recommendations for more robust architectures
Role of learning strategies in robustness
Generalizability
Extending the study to different CNN architectures
Cross-validation with diverse datasets
Conclusion
Summary of key insights on adversarial robustness
Importance of the proposed visualization tools
Call to action for further research in the field
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of adversarial examples compromising the robustness of Artificial Neural Networks (ANNs) by proposing a method to quantify and visually analyze the discrepancies between the latent representations of clean and adversarial samples in different layers of Convolutional Neural Networks (CNNs) . This problem is not new, as there is an extensive literature showing the vulnerability of both manually-designed ANNs and those designed in an automated manner to adversarial attacks . The proposed approach focuses on exploring weaknesses in ANNs when faced with adversarial examples, emphasizing the importance of understanding these vulnerabilities for defense development .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the robustness of Artificial Neural Networks (ANNs) against adversarial attacks . The study focuses on evaluating the vulnerability of manually-designed ANNs as well as ANNs designed in an automated manner to adversarial attacks . The research seeks to quantify and visually examine the differences in latent representations of clean and adversarial samples across different layers of ANNs, particularly Convolutional Neural Networks (CNNs) used for image classification tasks . The hypothesis revolves around understanding the impact of adversarial perturbations on the inner workings of ANNs and aims to propose a method for visualizing and analyzing changes in the representation of input data as it traverses through various layers of an ANN .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel method to analyze the robustness of Artificial Neural Networks (ANNs) against adversarial examples by quantifying and visually examining the discrepancies between the latent representations of clean and adversarial samples . This method focuses on multi-layer analysis, revealing that discrepancies between clean and perturbed data are present even during feature extraction, before the final convolutional layer . The paper introduces a layerwise robustness metric that aids in defense development, potentially improving fitness functions or selecting layers for detection-based defenses .

Furthermore, the paper suggests evaluating the proposed approach on more datasets and with models explicitly designed to be adversarially robust in the future . The method involves selecting and performing an adversarial attack on correctly classified images, passing both perturbed and clean images through the ANN to extract hidden representations up to the desired target layer, and applying the t-distributed Stochastic Neighbor Embedding (t-SNE) method to visualize the extracted representations in a two-dimensional map . This visualization helps in comparing clean and perturbed image embeddings to measure layer robustness .

The paper also discusses the use of white-box attacks, specifically 𝐿∞ perturbations with 𝜖 = 8/255 and 𝐿2 perturbations with 𝜖 = 0.5, along with the Auto-PGD method for attacks . It highlights that network deterioration begins in the feature extraction layers, impacting how CNNs differentiate between clean and perturbed images, which is reflected in their separation on the t-SNE maps . Additionally, the paper emphasizes the importance of attacking instances from multiple classes to detect overlaps on the t-SNE map, indicating layerwise robustness . The proposed method for analyzing the adversarial robustness of Artificial Neural Networks (ANNs) offers several key characteristics and advantages compared to previous methods outlined in the paper :

  1. Layerwise Analysis: The method focuses on multi-layer analysis, examining the discrepancies between the latent representations of clean and adversarial samples at different layers of Convolutional Neural Networks (CNNs) . This layerwise approach allows for a detailed understanding of how network deterioration begins in the feature extraction layers, impacting the model's ability to differentiate between clean and perturbed images .

  2. Visualization with t-SNE: The method utilizes the t-distributed Stochastic Neighbor Embedding (t-SNE) technique for visual inspection, enabling the quantification of differences between original and altered data as it progresses through the network layers . This visualization aids in measuring layer robustness by comparing clean and perturbed image embeddings, providing insights into the inner workings of the model .

  3. Robustness Metric: A robustness metric is proposed based on the differences between clean and adversarially perturbed representations on the t-SNE map . This metric helps in evaluating the network's robustness by measuring the overlap between clean and perturbed image embeddings, indicating layerwise robustness .

  4. Evaluation on Datasets and Models: The method suggests evaluating the approach on more datasets and with models explicitly designed to be adversarially robust in the future . This approach ensures the generalizability and effectiveness of the proposed method across different datasets and model architectures.

  5. Attack Strategies: The method employs white-box attacks, specifically 𝐿∞ and 𝐿2 perturbations, using the Auto-PGD method for attacks . By considering different attack strategies, the method provides a comprehensive evaluation of the model's robustness against adversarial examples.

  6. Experimental Setup: The experiments are conducted using Python with TensorFlow and PyTorch, utilizing the CIFAR-10 dataset and various model architectures . The method ensures data balance through stratification and includes attacks on correctly classified images for a fair comparison between models.

In conclusion, the proposed method offers a comprehensive and systematic approach to analyzing the adversarial robustness of ANNs, providing insights into network vulnerabilities at different layers and offering a robustness metric for evaluation. By leveraging visualization techniques and robust attack strategies, this method contributes to advancing the understanding and defense development against adversarial attacks in neural networks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of exploring layerwise adversarial robustness. Noteworthy researchers in this area include Aleksander Madry, Alexey Kurakin, Nicholas Carlini, David A. Wagner, Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Zhao, Heather Zheng, Prateek Mittal, Francesco Croce, Maksym Andriushchenko, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Matthias Hein, Chaitanya Devaguptapu, Devansh Agarwal, Gaurav Mittal, Pulkit Gopalani, Vineeth N Balasubramanian, Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy, Inês Valentim, Nuno Lourenço, Nuno Antunes, Laurens van der Maaten, Geoffrey Hinton, Sergey Zagoruyko, Nikos Komodakis, and many others .

The key to the solution mentioned in the paper involves analyzing the different layers of a Convolutional Neural Network (CNN) from an adversarial robustness perspective. The proposed methodology includes selecting and performing adversarial attacks, extracting hidden representations from the ANN, applying the t-distributed Stochastic Neighbor Embedding (t-SNE) method to visualize the representations, and computing a metric that measures the overlap between clean and perturbed image embeddings in the t-SNE space to quantify layer robustness . This approach helps in identifying weak spots in the layers early on, aiding in defense development and potentially improving fitness functions in NeuroEvolution approaches or selecting layers for detection-based defenses .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted using Python 3.8, Tensorflow 2.5.0, and PyTorch 1.10.1 .
  • The CIFAR-10 dataset's test set was split into a validation set and a final test set, each containing 5000 images, with adversarial examples generated for the validation images .
  • White-box attacks were performed with 𝐿∞ perturbations of 𝜖 = 8/255 and 𝐿2 perturbations of 𝜖 = 0.5 .
  • The experiments included using a CNN designed by NE and a handcrafted architecture as a baseline, specifically the WRN-28-10 model trained by the RobustBench team .
  • The methodology involved selecting and performing adversarial attacks on correctly classified images, extracting hidden representations from the ANN up to the desired target layer, and applying the t-SNE method to visualize the representations .
  • The robustness metric proposed in the paper was based on comparing the differences between the clean and adversarially perturbed representations on the t-SNE map .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the CIFAR-10 dataset, which was split into a validation set and a final test set, each containing 5000 images . The code for the experiments conducted in the study is open source and can be accessed through the following links:


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified regarding adversarial robustness in Artificial Neural Networks (ANNs) . The study explores the vulnerabilities of ANNs to adversarial examples, which are crafted to deceive the models by introducing imperceptible perturbations to benign data samples . By conducting experiments using pre-trained models without re-training, the paper investigates the discrepancies between clean and perturbed data representations across different layers of Convolutional Neural Networks (CNNs) .

The research methodology involves analyzing the latent representations of clean and adversarially perturbed images in various layers of the CNNs using the t-distributed Stochastic Neighbor Embedding (t-SNE) technique . This approach allows for a visual examination of the differences between original and altered data, providing insights into the inner workings of the models . The experiments focus on evaluating the robustness of the models against 𝐿2 and 𝐿∞ perturbations, revealing that network deterioration begins in the feature extraction layers, impacting the models' ability to distinguish between clean and perturbed images .

Furthermore, the results demonstrate that the discrepancies between clean and perturbed representations emerge early on in the feature extraction layers of the CNNs, affecting subsequent classification . The visual analysis of the t-SNE maps supports the findings obtained through the proposed robustness metric, which measures the differences between clean and perturbed embeddings to identify weak spots in the layers . Overall, the experiments and results in the paper provide substantial evidence to validate the scientific hypotheses related to adversarial robustness in ANNs and offer valuable insights for defense development and model evaluation .


What are the contributions of this paper?

The contributions of the paper "Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE" include:

  • Proposing a method to quantify and visually examine the discrepancies between the latent representations of clean and adversarial samples in neural networks .
  • Introducing a layerwise robustness metric that aids in defense development and can be used to improve fitness functions in neuroevolution or select layers for detection-based defenses .
  • Conducting experiments on the CIFAR-10 dataset using various models, including a CNN designed by NeuroEvolution, to analyze adversarial robustness .
  • Utilizing white-box attacks with 𝐿∞ and 𝐿2 perturbations to evaluate the robustness of the models .
  • Demonstrating that network deterioration due to adversarial attacks begins in the feature extraction layers, impacting the ability of CNNs to differentiate between clean and perturbed images .

What work can be continued in depth?

Further research in this area can delve deeper into evaluating the robustness of Artificial Neural Networks (ANNs) against adversarial examples. One avenue for future work is to expand the evaluation of the proposed method on additional datasets beyond CIFAR-10 and with models explicitly designed for adversarial robustness . Additionally, exploring the impact of different learning strategies on the adversarial robustness of models could be a valuable direction for further investigation . Furthermore, the study suggests potential applications of the layerwise robustness metric in enhancing NeuroEvolution fitness functions or selecting layers for detection-based defenses, indicating a promising area for future research .

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