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 876–900 of 1746 papers

Title	Date	Tasks	Status	Hype
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy	Jul 26, 2022	Adversarial RobustnessFine-Grained Image Classification	CodeCode Available	1
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
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility	Jul 22, 2022	Adversarial RobustnessSelf-Supervised Learning	—Unverified	0
Do Perceptually Aligned Gradients Imply Adversarial Robustness?	Jul 22, 2022	Adversarial RobustnessImage Classification	CodeCode Available	0
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance	Jul 21, 2022	Adversarial Robustness	—Unverified	0
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training	Jul 21, 2022	Adversarial Robustness	—Unverified	0
Careful What You Wish For: on the Extraction of Adversarially Trained Models	Jul 21, 2022	Adversarial Robustness	CodeCode Available	0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks	Jul 20, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Tailoring Self-Supervision for Supervised Learning	Jul 20, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
Assaying Out-Of-Distribution Generalization in Transfer Learning	Jul 19, 2022	Adversarial RobustnessOut-of-Distribution Generalization	CodeCode Available	0
Adversarial Contrastive Learning via Asymmetric InfoNCE	Jul 18, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Automated Repair of Neural Networks	Jul 17, 2022	Adversarial Robustness	CodeCode Available	0
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
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures	Jul 12, 2022	Adversarial RobustnessMultiobjective Optimization	—Unverified	0
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information	Jul 12, 2022	Adversarial Robustness	CodeCode Available	0
Certified Adversarial Robustness via Anisotropic Randomized Smoothing	Jul 12, 2022	Adversarial RobustnessAll	—Unverified	0
Adversarial Robustness Assessment of NeuroEvolution Approaches	Jul 12, 2022	Adversarial Robustnessimage-classification	—Unverified	0
RUSH: Robust Contrastive Learning via Randomized Smoothing	Jul 11, 2022	Adversarial RobustnessContrastive Learning	—Unverified	0
Dynamic Time Warping based Adversarial Framework for Time-Series Domain	Jul 9, 2022	Adversarial RobustnessDynamic Time Warping	CodeCode Available	0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy	Jul 8, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network	Jul 7, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Robustness of Visual Dialog	Jul 6, 2022	Adversarial RobustnessVisual Dialog	—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