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Interpreting Adversarial Examples in Deep Learning: A Review
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Outgoing claims: the papers this work cites, builds on, or argues with.
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An axiomatic approach to the concept of interaction among players in cooperative games
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Robustness to adversarial examples can be improved with overfitting
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A theory of diagnosis from first principles
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Correction of AI systems by linear discriminants: Probabilistic foundations
inferredcites →https://doi.org/10.1016/j.patcog.2020.107332openalex-snapshot
Understanding adversarial attacks on deep learning based medical image analysis systems
inferredcites →https://doi.org/10.1016/j.strusafe.2008.06.020openalex-snapshot
Aleatory or epistemic? Does it matter?
inferredcites →https://doi.org/10.1101/2020.06.16.154542openalex-snapshot
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/bigdata55660.2022openalex-snapshot
2022 IEEE International Conference on Big Data (Big Data)
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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00396openalex-snapshot
Empirical Study of the Topology and Geometry of Deep Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr.2019.00059openalex-snapshot
Feature Denoising for Improving Adversarial Robustness
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00040openalex-snapshot
Benchmarking Adversarial Robustness on Image Classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00108openalex-snapshot
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00871openalex-snapshot
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvprw.2018.00210openalex-snapshot
On Visible Adversarial Perturbations & Digital Watermarking
inferredcites →https://doi.org/10.1109/iccv.2019.00303openalex-snapshot
Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation
inferredcites →https://doi.org/10.1109/icdm.2015.84openalex-snapshot
A Unified Gradient Regularization Family for Adversarial Examples
inferredcites →https://doi.org/10.1109/icip40778.2020openalex-snapshot
2020 IEEE International Conference on Image Processing (ICIP)
inferredcites →https://doi.org/10.1109/icmla.2016.0045openalex-snapshot
Are Accuracy and Robustness Correlated
inferredcites →https://doi.org/10.1109/icpr48806.2021openalex-snapshot
2020 25th International Conference on Pattern Recognition (ICPR)
inferredcites →https://doi.org/10.1109/ijcnn48605.2020openalex-snapshot
2020 International Joint Conference on Neural Networks (IJCNN)
inferredcites →https://doi.org/10.1109/infocom42981.2021.9488754openalex-snapshot
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/sp.2019.00044openalex-snapshot
Certified Robustness to Adversarial Examples with Differential Privacy
inferredcites →https://doi.org/10.1109/tdsc.2022.3194642openalex-snapshot
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1109/tip.2020.2975918openalex-snapshot
Principal Component Adversarial Example
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3128572.3140444openalex-snapshot
Adversarial Examples Are Not Easily Detected
inferredcites →https://doi.org/10.1145/3372297.3417253openalex-snapshot
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
inferredcites →https://doi.org/10.14722/ndss.2018.23198openalex-snapshot
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i10.17039openalex-snapshot
Large Norms of CNN Layers Do Not Hurt Adversarial Robustness
inferredcites →https://doi.org/10.1609/aaai.v35i11.17187openalex-snapshot
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
inferredcites →https://doi.org/10.1609/aaai.v35i4.16441openalex-snapshot
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective
inferredcites →https://doi.org/10.24963/ijcai.2019/415openalex-snapshot
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness
inferredcites →https://doi.org/10.24963/ijcai.2019/583openalex-snapshot
Interpreting and Evaluating Neural Network Robustness
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.4230/oasics.ng-res.2022.1openalex-snapshot
Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.1206.6389openalex-snapshot
Poisoning Attacks against Support Vector Machines
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1511.06292openalex-snapshot
Foveation-based Mechanisms Alleviate Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1608.00853openalex-snapshot
A study of the effect of JPG compression on adversarial images
inferredcites →https://doi.org/10.48550/arxiv.1608.08967openalex-snapshot
Robustness of classifiers: from adversarial to random noise
inferredcites →https://doi.org/10.48550/arxiv.1612.08220openalex-snapshot
Understanding Neural Networks through Representation Erasure
inferredcites →https://doi.org/10.48550/arxiv.1704.03453openalex-snapshot
The Space of Transferable Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.1708.05493openalex-snapshot
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1708.06733openalex-snapshot
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
inferredcites →https://doi.org/10.48550/arxiv.1710.10766openalex-snapshot
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1711.00117openalex-snapshot
Countering Adversarial Images using Input Transformations
inferredcites →https://doi.org/10.48550/arxiv.1711.01991openalex-snapshot
Mitigating Adversarial Effects Through Randomization
inferredcites →https://doi.org/10.48550/arxiv.1711.02846openalex-snapshot
Intriguing Properties of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1712.04248openalex-snapshot
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1801.02613openalex-snapshot
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
inferredcites →https://doi.org/10.48550/arxiv.1801.09344openalex-snapshot
Certified Defenses against Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1801.10578openalex-snapshot
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
inferredcites →https://doi.org/10.48550/arxiv.1803.01442openalex-snapshot
Stochastic Activation Pruning for Robust Adversarial Defense
inferredcites →https://doi.org/10.48550/arxiv.1803.09638openalex-snapshot
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1804.11285openalex-snapshot
Adversarially Robust Generalization Requires More Data
inferredcites →https://doi.org/10.48550/arxiv.1807.01697openalex-snapshot
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
inferredcites →https://doi.org/10.48550/arxiv.1807.04457openalex-snapshot
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
inferredcites →https://doi.org/10.48550/arxiv.1809.08758openalex-snapshot
Low Frequency Adversarial Perturbation
inferredcites →https://doi.org/10.48550/arxiv.1810.11580openalex-snapshot
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1810.12272openalex-snapshot
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
inferredcites →https://doi.org/10.48550/arxiv.1901.08360openalex-snapshot
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1901.09035openalex-snapshot
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1901.10513openalex-snapshot
Adversarial Examples Are a Natural Consequence of Test Error in Noise
inferredcites →https://doi.org/10.48550/arxiv.1901.10861openalex-snapshot
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance
inferredcites →https://doi.org/10.48550/arxiv.1905.00877openalex-snapshot
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
inferredcites →https://doi.org/10.48550/arxiv.1905.02161openalex-snapshot
Batch Normalization is a Cause of Adversarial Vulnerability
inferredcites →https://doi.org/10.48550/arxiv.1905.02175openalex-snapshot
Adversarial Examples Are Not Bugs, They Are Features
inferredcites →https://doi.org/10.48550/arxiv.1905.12202openalex-snapshot
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
inferredcites →https://doi.org/10.48550/arxiv.1906.04392openalex-snapshot
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1909.09552openalex-snapshot
Defending Against Physically Realizable Attacks on Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1909.10147openalex-snapshot
Robust Local Features for Improving the Generalization of Adversarial Training
inferredcites →https://doi.org/10.48550/arxiv.2002.05990openalex-snapshot
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
inferredcites →https://doi.org/10.48550/arxiv.2002.10733openalex-snapshot
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
inferredcites →https://doi.org/10.48550/arxiv.2002.11318openalex-snapshot
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2005.03141openalex-snapshot
Towards Frequency-Based Explanation for Robust CNN
inferredcites →https://doi.org/10.48550/arxiv.2006.16427openalex-snapshot
Biologically Inspired Mechanisms for Adversarial Robustness
inferredcites →https://doi.org/10.48550/arxiv.2010.04055openalex-snapshot
A Unified Approach to Interpreting and Boosting Adversarial Transferability
inferredcites →https://doi.org/10.48550/arxiv.2010.14927openalex-snapshot
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations
inferredcites →https://doi.org/10.48550/arxiv.2102.05311openalex-snapshot
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
inferredcites →https://doi.org/10.48550/arxiv.2103.08307openalex-snapshot
Improving Adversarial Robustness via Channel-wise Activation Suppressing
inferredcites →https://doi.org/10.48550/arxiv.2104.09658openalex-snapshot
Calibration and Consistency of Adversarial Surrogate Losses
inferredcites →https://doi.org/10.48550/arxiv.2106.12611openalex-snapshot
Adversarial Examples in Multi-Layer Random ReLU Networks
inferredcites →https://doi.org/10.48550/arxiv.2204.02735openalex-snapshot
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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SOOM: A Schedule-Search-Based Operator Obfuscation Method Against Model Extraction Attacks
inferred← citeshttps://doi.org/10.1109/tifs.2026.3702560openalex-snapshot
Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector
inferred← citeshttps://doi.org/10.1109/tifs.2026.3712205openalex-snapshot
RFA-Tex: Range-Flexible Adaptive Physical Adversarial Texture Against Real-World Person Detectors
inferred← citeshttps://doi.org/10.1109/tip.2026.3709493openalex-snapshot
Adversarial Robustness in Zero-Shot Learning: An Empirical Study on Class and Concept-Level Vulnerabilities
inferred← citeshttps://doi.org/10.1109/tip.2026.3732307openalex-snapshot
Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense
inferred← citeshttps://doi.org/10.1109/tiv.2024.3351271openalex-snapshot
Efficient and Robust Object Detection Against Multi-Type Corruption Using Complete Edge Based on Lightweight Parameter Isolation
inferred← citeshttps://doi.org/10.1109/tkde.2026.3709786openalex-snapshot
Data-Centric Challenges, Techniques, and Impacts: A Survey on Image Data Perturbation
inferred← citeshttps://doi.org/10.1109/tnnls.2025.3649055openalex-snapshot
Accurate and Robust Neural Architecture Search via a Flexible Supernet
inferred← citeshttps://doi.org/10.1117/12.3059926openalex-snapshot
Big data mining algorithm for malicious behavior feature extraction and application in network security
inferred← citeshttps://doi.org/10.1145/3627817openalex-snapshot
It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness
inferred← citeshttps://doi.org/10.1145/3648351openalex-snapshot
Adversarial Robustness of Neural Networks from the Perspective of Lipschitz Calculus: A Survey
inferred← citeshttps://doi.org/10.1145/3729706.3729813openalex-snapshot
Towards a Novel Adversarial Perturbations Analysis Driven by Frequency
inferred← citeshttps://doi.org/10.1145/3746027.3755068openalex-snapshot
Reversible Privacy Preserving on Vision-Language Models via Adversarial Multimodal Key
inferred← citeshttps://doi.org/10.1145/3785666openalex-snapshot
Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
inferred← citeshttps://doi.org/10.1145/3828753openalex-snapshot
Deep Neural Networks Inspired by Differential Equations
inferred← citeshttps://doi.org/10.1177/03611981251356511openalex-snapshot
Intent-Driven Controllable Adversarial Attacks against Multi-Object Trackers for Intelligent Transportation Systems
inferred← citeshttps://doi.org/10.1360/ssi-2025-0205openalex-snapshot
A detection method against adversarial example attack based on neural network decision boundary and consistency analysis
inferred← citeshttps://doi.org/10.15622/ia.24.6.8openalex-snapshot
Analysis of Modern Research on Protection against Adversarial Attacks in Energy Systems
inferred← citeshttps://doi.org/10.2139/ssrn.6878397openalex-snapshot
Deep Learning-Driven Intelligent Analysis and Detection of Encrypted Malicious Traffic: A Survey
inferred← citeshttps://doi.org/10.2196/59370openalex-snapshot
Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.2298/csis230907045jopenalex-snapshot
Semantic feature-based test selection for deep neural networks: A frequency domain perspective
inferred← citeshttps://doi.org/10.23919/ccc63176.2024.10661385openalex-snapshot
A Regularization Method for Enhancing the Robustness of Regression Networks
inferred← citeshttps://doi.org/10.23919/cje.2022.00.451openalex-snapshot
DeepLogic: Priority Testing of Deep Learning Through Interpretable Logic Units
inferred← citeshttps://doi.org/10.32604/cmc.2024.058675openalex-snapshot
Assessor Feedback Mechanism for Machine Learning Model
inferred← citeshttps://doi.org/10.3389/fcomp.2023.1274832openalex-snapshot
Leveraging linear mapping for model-agnostic adversarial defense
inferred← citeshttps://doi.org/10.3389/fdata.2024.1467222openalex-snapshot
Establishing and evaluating trustworthy AI: overview and research challenges
inferred← citeshttps://doi.org/10.3390/fi17100459openalex-snapshot
Adversarial Robustness Evaluation for Multi-View Deep Learning Cybersecurity Anomaly Detection
inferred← citeshttps://doi.org/10.3390/math14071095openalex-snapshot
Scalable One-Pixel Attacks on Deep Neural Networks for High-Resolution Images
inferred← citeshttps://doi.org/10.3390/rs16224277openalex-snapshot
A Gradual Adversarial Training Method for Semantic Segmentation
inferred← citeshttps://doi.org/10.3390/s24123848openalex-snapshot
Adversarial Attacks on Intrusion Detection Systems in In-Vehicle Networks of Connected and Autonomous Vehicles
inferred← citeshttps://doi.org/10.3390/su151813401openalex-snapshot
Blockchain Technology and Related Security Risks: Towards a Seven-Layer Perspective and Taxonomy
inferred← citeshttps://doi.org/10.36227/techrxiv.175037432.26316641/v1openalex-snapshot
A Survey on Image Data Perturbations: Challenges, Techniques, and Impacts
inferred← citeshttps://doi.org/10.4018/979-8-2600-0156-1.ch009openalex-snapshot
Quantum-Enhanced Adversarial Robustness in Artificial Intelligence
inferred← citeshttps://doi.org/10.70675/0b9d3f17zbd47z4bf8zac5fz955f8a1e85fcopenalex-snapshot
Statistical Understanding of Adversarial Robustness
inferred← citeshttps://doi.org/10.70675/690e25b2z1fb9z4d7fza6cbzcb09e2b773cfopenalex-snapshot
Neural networks security under realistic scenario
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