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 101–125 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks	Nov 1, 2022	Adversarial RobustnessPseudo Label	CodeCode Available	1	5
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks	Jan 31, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available	1	5
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial vulnerability of powerful near out-of-distribution detection	Jan 18, 2022	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	1	5
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion	Mar 29, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization	Mar 18, 2024	Adversarial RobustnessImage Quality Assessment	CodeCode Available	1	5
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Achieving robustness in classification using optimal transport with hinge regularization	Jun 11, 2020	Adversarial RobustnessBinary Classification	CodeCode Available	1	5
Adversarial Machine Learning: Bayesian Perspectives	Mar 7, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures	Jan 14, 2025	Adversarial Robustness	CodeCode Available	1	5
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration	Mar 23, 2021	Adversarial RobustnessDimensionality Reduction	CodeCode Available	1	5
Efficient Exact Verification of Binarized Neural Networks	May 7, 2020	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Prompt Tuning for Vision-Language Models	Nov 19, 2023	Adversarial Robustness	CodeCode Available	1	5
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness	Sep 2, 2024	Adversarial RobustnessNetwork Pruning	CodeCode Available	1	5
Adversarial Reasoning at Jailbreaking Time	Feb 3, 2025	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Enhancing adversarial robustness in Natural Language Inference using explanations	Sep 11, 2024	Adversarial RobustnessNatural Language Inference	CodeCode Available	1	5
Adversarial Robustification via Text-to-Image Diffusion Models	Jul 26, 2024	Adversarial Robustnesszero-shot-classification	CodeCode Available	1	5
Enhancing Adversarial Robustness via Test-time Transformation Ensembling	Jul 29, 2021	Adversarial Robustness	CodeCode Available	1	5
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses	Feb 28, 2022	Adversarial Robustnessimage-classification	CodeCode Available	1	5
Adversarial Image Color Transformations in Explicit Color Filter Space	Nov 12, 2020	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5

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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