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 1601–1625 of 1746 papers

Title	Date	Tasks	Status
Evaluating the Adversarial Robustness for Fourier Neural Operators	Apr 8, 2022	Adversarial Robustnessscientific discovery	—Unverified
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge	Oct 27, 2021	Adversarial RobustnessBinarization	—Unverified
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric	Jun 6, 2019	Adversarial Robustness	—Unverified
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples	Feb 9, 2021	Adversarial Robustness	—Unverified
Evaluating the Adversarial Robustness of Detection Transformers	Dec 25, 2024	Adversarial RobustnessAutonomous Driving	—Unverified
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN	Oct 12, 2021	Adversarial RobustnessFoveation	—Unverified
Evaluating the Evaluators: Trust in Adversarial Robustness Tests	Jul 4, 2025	Adversarial Robustness	—Unverified
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification	Jan 10, 2021	Adversarial RobustnessGeneral Classification	—Unverified
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness	Dec 4, 2023	Adversarial Robustness	—Unverified
Evaluation Methodology for Attacks Against Confidence Thresholding Models	May 1, 2019	Adversarial RobustnessBenchmarking	—Unverified
Understanding Adversarially Robust Generalization via Weight-Curvature Index	Oct 10, 2024	Adversarial RobustnessGeneralization Bounds	—Unverified
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions	Feb 20, 2024	Adversarial RobustnessEvolutionary Algorithms	—Unverified
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions	Dec 1, 2021	Adversarial RobustnessAutonomous Driving	—Unverified
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases	Feb 28, 2023	Adversarial RobustnessNatural Language Inference	—Unverified
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures	Jul 12, 2022	Adversarial RobustnessMultiobjective Optimization	—Unverified
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification	Jun 18, 2022	Adversarial RobustnessBinary Classification	—Unverified
Adversarial Learning with Cost-Sensitive Classes	Jan 29, 2021	Adversarial Robustness	—Unverified
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor	May 22, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Biased Multi-Domain Adversarial Training	Sep 29, 2021	Adversarial Robustness	—Unverified
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks	Apr 14, 2025	Adversarial RobustnessBinary Classification	—Unverified
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks	Jul 30, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Smoothing Policy Iteration for Zero-sum Markov Games	Dec 3, 2022	Adversarial Robustness	—Unverified
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients	Jan 1, 2020	Adversarial Robustness	—Unverified
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness	Sep 15, 2022	Adversarial Robustness	—Unverified
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness	Mar 25, 2019	Adversarial Robustness	—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