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 126–150 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1
A Self-supervised Approach for Adversarial Robustness	Jun 8, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	1
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures	Jan 14, 2025	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1
A Light Recipe to Train Robust Vision Transformers	Sep 15, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1
Adversarial vulnerability of powerful near out-of-distribution detection	Jan 18, 2022	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	1
Adversarial Visual Robustness by Causal Intervention	Jun 17, 2021	Adversarial Robustness	CodeCode Available	1
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks	May 19, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion	Mar 29, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection	Mar 12, 2020	Adversarial RobustnessNovelty Detection	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
Are Transformers More Robust Than CNNs?	Nov 10, 2021	Adversarial Robustness	CodeCode Available	1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks	Jan 31, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available	1
Adversarial Contrastive Learning via Asymmetric InfoNCE	Jul 18, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies	Apr 14, 2024	Adversarial Robustness	CodeCode Available	1
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective	Aug 21, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
Adversarial Robustness as a Prior for Learned Representations	Jun 3, 2019	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication	Dec 13, 2024	Adversarial Robustness	CodeCode Available	1
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks	Nov 1, 2022	Adversarial RobustnessPseudo Label	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