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 226–250 of 1746 papers

Title	Date	Tasks	Status	Hype
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1
Part-Based Models Improve Adversarial Robustness	Sep 15, 2022	Adversarial Robustness	CodeCode Available	1
PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition	Jul 15, 2024	Adversarial RobustnessInductive Bias	CodeCode Available	1
PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor	Mar 11, 2024	Adversarial Robustness	CodeCode Available	1
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models	Jun 22, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Bispectral Neural Networks	Sep 7, 2022	Adversarial RobustnessRepresentation Learning	CodeCode Available	1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors	May 15, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
Adversarial Prompt Tuning for Vision-Language Models	Nov 19, 2023	Adversarial Robustness	CodeCode Available	1
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
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP	Mar 5, 2025	Adversarial RobustnessImage-text matching	CodeCode Available	1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Reasoning at Jailbreaking Time	Feb 3, 2025	Adversarial Robustness	CodeCode Available	1
Pruning Adversarially Robust Neural Networks without Adversarial Examples	Oct 9, 2022	Adversarial Robustness	CodeCode Available	1
Enhancing adversarial robustness in Natural Language Inference using explanations	Sep 11, 2024	Adversarial RobustnessNatural Language Inference	CodeCode Available	1
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness via Promoting Ensemble Diversity	Jan 25, 2019	Adversarial RobustnessDiversity	CodeCode Available	1
Random Smoothing Might be Unable to Certify _ Robustness for High-Dimensional Images	Feb 10, 2020	Adversarial Robustness	CodeCode Available	1
A Light Recipe to Train Robust Vision Transformers	Sep 15, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
Adversarial Robustification via Text-to-Image Diffusion Models	Jul 26, 2024	Adversarial Robustnesszero-shot-classification	CodeCode Available	1
Reliable Adversarial Distillation with Unreliable Teachers	Jun 9, 2021	Adversarial Robustness	CodeCode Available	1
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks	Mar 3, 2020	Adversarial Robustness	CodeCode Available	1
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging	Aug 22, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures	Jan 14, 2025	Adversarial Robustness	CodeCode Available	1
Adversarial Image Color Transformations in Explicit Color Filter Space	Nov 12, 2020	Adversarial Robustness	CodeCode Available	1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum	Jun 21, 2022	Adversarial RobustnessDeep Reinforcement 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