Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 751–775 of 1746 papers

Title	Date	Tasks	Status
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data	Nov 8, 2023	Adversarial Robustness	—Unverified
Measuring Adversarial Datasets	Nov 6, 2023	Adversarial RobustnessDiversity	—Unverified
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness	Oct 30, 2023	Adversarial Robustnesscounterfactual	—Unverified
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow	Oct 26, 2023	Adversarial RobustnessMotion Detection	CodeCode Available
Deceptive Fairness Attacks on Graphs via Meta Learning	Oct 24, 2023	Adversarial RobustnessFairness	CodeCode Available
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval	Oct 23, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns	Oct 23, 2023	Adversarial RobustnessDisentanglement	—Unverified
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation	Oct 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers	Oct 19, 2023	Adversarial Robustness	—Unverified
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified
A Non-monotonic Smooth Activation Function	Oct 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches	Oct 11, 2023	Adversarial Robustness	—Unverified
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks	Oct 10, 2023	Adversarial Robustness	—Unverified
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE	Oct 10, 2023	Adversarial RobustnessDensity Estimation	—Unverified
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization	Oct 9, 2023	Adversarial RobustnessGeneralization Bounds	—Unverified
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria	Oct 5, 2023	Adversarial AttackAdversarial Defense	CodeCode Available
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions	Oct 4, 2023	Adversarial Robustness	—Unverified
Can Language Models be Instructed to Protect Personal Information?	Oct 3, 2023	Adversarial RobustnessRed Teaming	—Unverified
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework	Oct 3, 2023	Adversarial RobustnessScheduling	—Unverified
Understanding Robust Overfitting from the Feature Generalization Perspective	Oct 1, 2023	Adversarial RobustnessData Augmentation	—Unverified
Adversarial Machine Learning in Latent Representations of Neural Networks	Sep 29, 2023	Adversarial RobustnessEdge-computing	CodeCode Available
Intrinsic Biologically Plausible Adversarial Robustness	Sep 29, 2023	Adversarial Robustness	—Unverified
On the Trade-offs between Adversarial Robustness and Actionable Explanations	Sep 28, 2023	Adversarial Robustness	—Unverified
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness	Sep 28, 2023	Adversarial Robustness	—Unverified

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified