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 151–175 of 1746 papers

Title	Date	Tasks	Status	Hype
Improving Adversarial Robustness via Mutual Information Estimation	Jul 25, 2022	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness	Jul 22, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Tailoring Self-Supervision for Supervised Learning	Jul 20, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
Adversarial Contrastive Learning via Asymmetric InfoNCE	Jul 18, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Distance Learner: Incorporating Manifold Prior to Model Training	Jul 14, 2022	Adversarial Robustnessmodel	CodeCode Available	1
Adversarially-Aware Robust Object Detector	Jul 13, 2022	Adversarial RobustnessObject	CodeCode Available	1
Removing Batch Normalization Boosts Adversarial Training	Jul 4, 2022	Adversarial Robustness	CodeCode Available	1
(Certified!!) Adversarial Robustness for Free!	Jun 21, 2022	Adversarial RobustnessDenoising	CodeCode Available	1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum	Jun 21, 2022	Adversarial RobustnessDeep Reinforcement Learning	CodeCode Available	1
Towards Adversarial Attack on Vision-Language Pre-training Models	Jun 19, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Understanding Robust Overfitting of Adversarial Training and Beyond	Jun 17, 2022	Adversarial RobustnessData Ablation	CodeCode Available	1
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models	Jun 9, 2022	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
FedNest: Federated Bilevel, Minimax, and Compositional Optimization	May 4, 2022	Adversarial RobustnessHyperparameter Optimization	CodeCode Available	1
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning	May 1, 2022	Adversarial Robustnesstext-classification	CodeCode Available	1
Engineering flexible machine learning systems by traversing functionally-invariant paths	Apr 30, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck	Apr 6, 2022	Adversarial Robustness	CodeCode Available	1
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network	Apr 6, 2022	Adversarial RobustnessModel Compression	CodeCode Available	1
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective	Mar 27, 2022	Adversarial Robustnessimage-classification	CodeCode Available	1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack	Mar 10, 2022	Adversarial Robustness	CodeCode Available	1
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4	Mar 8, 2022	Adversarial Robustness	CodeCode Available	1
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches	Mar 7, 2022	Adversarial RobustnessBenchmarking	CodeCode Available	1
Enhancing Adversarial Robustness for Deep Metric Learning	Mar 2, 2022	Adversarial RobustnessMetric Learning	CodeCode Available	1

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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