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 501–525 of 1746 papers

Title	Date	Tasks	Status	Hype
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns	Oct 23, 2023	Adversarial RobustnessDisentanglement	—Unverified	0
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval	Oct 23, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers	Oct 19, 2023	Adversarial Robustness	—Unverified	0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation	Oct 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift	Oct 19, 2023	Adversarial RobustnessBenchmarking	CodeCode Available	1
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks	Oct 18, 2023	Adversarial Robustness	CodeCode Available	1
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now	Oct 18, 2023	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified	0
A Non-monotonic Smooth Activation Function	Oct 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization	Oct 12, 2023	Adversarial RobustnessHyperparameter Optimization	CodeCode Available	1
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches	Oct 11, 2023	Adversarial Robustness	—Unverified	0
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE	Oct 10, 2023	Adversarial RobustnessDensity Estimation	—Unverified	0
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks	Oct 10, 2023	Adversarial Robustness	—Unverified	0
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization	Oct 9, 2023	Adversarial RobustnessGeneralization Bounds	—Unverified	0
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models	Oct 7, 2023	Adversarial Robustness	CodeCode Available	1
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria	Oct 5, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	0
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions	Oct 4, 2023	Adversarial Robustness	—Unverified	0
Can Language Models be Instructed to Protect Personal Information?	Oct 3, 2023	Adversarial RobustnessRed Teaming	—Unverified	0
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework	Oct 3, 2023	Adversarial RobustnessScheduling	—Unverified	0
Understanding Robust Overfitting from the Feature Generalization Perspective	Oct 1, 2023	Adversarial RobustnessData Augmentation	—Unverified	0
Adversarial Machine Learning in Latent Representations of Neural Networks	Sep 29, 2023	Adversarial RobustnessEdge-computing	CodeCode Available	0
Intrinsic Biologically Plausible Adversarial Robustness	Sep 29, 2023	Adversarial Robustness	—Unverified	0
On the Trade-offs between Adversarial Robustness and Actionable Explanations	Sep 28, 2023	Adversarial Robustness	—Unverified	0

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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