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 901–925 of 1746 papers

Title	Date	Tasks	Status
Provable Defense Against Clustering Attacks on 3D Point Clouds	Nov 22, 2021	Adversarial RobustnessClustering	—Unverified
Provable Unrestricted Adversarial Training without Compromise with Generalizability	Jan 22, 2023	Adversarial Robustness	—Unverified
Provably Robust Transfer	Sep 29, 2021	Adversarial RobustnessTransfer Learning	—Unverified
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection	Oct 3, 2022	Active Speaker DetectionAdversarial Robustness	—Unverified
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability	Apr 14, 2022	Adversarial Robustness	—Unverified
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks	Apr 22, 2020	Adversarial RobustnessQuantization	—Unverified
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution	Oct 9, 2020	Adversarial RobustnessObject Recognition	—Unverified
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks	Mar 12, 2025	Adversarial RobustnessQuantization	—Unverified
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses	Nov 29, 2023	Adversarial RobustnessMulti-class Classification	—Unverified
Quantum Support Vector Regression for Robust Anomaly Detection	May 2, 2025	Adversarial RobustnessAnomaly Detection	—Unverified
Query complexity of adversarial attacks	Oct 2, 2020	Adversarial Robustness	—Unverified
Raising the Bar for Certified Adversarial Robustness with Diffusion Models	May 17, 2023	Adversarial Robustness	—Unverified
Random Entangled Tokens for Adversarially Robust Vision Transformer	Jan 1, 2024	Adversarial Robustness	—Unverified
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators	Feb 27, 2023	Adversarial Robustness	—Unverified
Random Projections for Improved Adversarial Robustness	Feb 18, 2021	Adversarial RobustnessDimensionality Reduction	—Unverified
Rapid Response: Mitigating LLM Jailbreaks with a Few Examples	Nov 12, 2024	Adversarial Robustness	—Unverified
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias	Sep 23, 2023	Adversarial Robustness	—Unverified
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment	Jan 30, 2024	Adversarial RobustnessEEG	—Unverified
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech	Sep 3, 2024	Adversarial RobustnessAutomatic Speech Recognition	—Unverified
RECAST: Interactive Auditing of Automatic Toxicity Detection Models	Jan 7, 2020	Adversarial RobustnessFairness	—Unverified
Recent Advances in Adversarial Training for Adversarial Robustness	Feb 2, 2021	Adversarial Robustness	—Unverified
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks	Nov 3, 2020	Adversarial Robustness	—Unverified
Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications	May 19, 2025	Adversarial RobustnessRecommendation Systems	—Unverified
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense	Jul 4, 2025	Adversarial Robustness	—Unverified
Reframing Neural Networks: Deep Structure in Overcomplete Representations	Mar 10, 2021	Adversarial RobustnessModel Selection	—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