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 1051–1075 of 1746 papers

Title	Date	Tasks	Status
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations	Aug 5, 2022	Adversarial RobustnessDiagnostic	CodeCode Available
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem	Aug 1, 2022	Adversarial Robustness	CodeCode Available
Is current research on adversarial robustness addressing the right problem?	Jul 31, 2022	Adversarial Robustness	—Unverified
Pro-tuning: Unified Prompt Tuning for Vision Tasks	Jul 28, 2022	Adversarial Robustnessimage-classification	—Unverified
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks	Jul 27, 2022	Adversarial RobustnessBenchmarking	—Unverified
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility	Jul 22, 2022	Adversarial RobustnessSelf-Supervised Learning	—Unverified
Do Perceptually Aligned Gradients Imply Adversarial Robustness?	Jul 22, 2022	Adversarial RobustnessImage Classification	CodeCode Available
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance	Jul 21, 2022	Adversarial Robustness	—Unverified
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training	Jul 21, 2022	Adversarial Robustness	—Unverified
Careful What You Wish For: on the Extraction of Adversarially Trained Models	Jul 21, 2022	Adversarial Robustness	CodeCode Available
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks	Jul 20, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Assaying Out-Of-Distribution Generalization in Transfer Learning	Jul 19, 2022	Adversarial RobustnessOut-of-Distribution Generalization	CodeCode Available
Automated Repair of Neural Networks	Jul 17, 2022	Adversarial Robustness	CodeCode Available
Adversarial Robustness Assessment of NeuroEvolution Approaches	Jul 12, 2022	Adversarial Robustnessimage-classification	—Unverified
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures	Jul 12, 2022	Adversarial RobustnessMultiobjective Optimization	—Unverified
Certified Adversarial Robustness via Anisotropic Randomized Smoothing	Jul 12, 2022	Adversarial RobustnessAll	—Unverified
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information	Jul 12, 2022	Adversarial Robustness	CodeCode Available
RUSH: Robust Contrastive Learning via Randomized Smoothing	Jul 11, 2022	Adversarial RobustnessContrastive Learning	—Unverified
Dynamic Time Warping based Adversarial Framework for Time-Series Domain	Jul 9, 2022	Adversarial RobustnessDynamic Time Warping	CodeCode Available
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy	Jul 8, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network	Jul 7, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Visual Dialog	Jul 6, 2022	Adversarial RobustnessVisual Dialog	—Unverified
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models	Jun 29, 2022	Adversarial RobustnessDiversity	CodeCode Available
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound	Jun 29, 2022	Adversarial Robustness	CodeCode Available
Increasing Confidence in Adversarial Robustness Evaluations	Jun 28, 2022	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