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 1451–1475 of 1746 papers

Title	Date	Tasks	Status
Pruning in the Face of Adversaries	Aug 19, 2021	Adversarial RobustnessNetwork Pruning	CodeCode Available
Improved Diffusion-based Generative Model with Better Adversarial Robustness	Feb 24, 2025	Adversarial RobustnessDenoising	CodeCode Available
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes	Mar 4, 2023	Adversarial RobustnessDiversity	CodeCode Available
Improved robustness to adversarial examples using Lipschitz regularization of the loss	Oct 1, 2018	Adversarial Robustness	CodeCode Available
Improved techniques for deterministic l2 robustness	Nov 15, 2022	Adversarial Robustness	CodeCode Available
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems	Oct 5, 2021	Adversarial Robustness	CodeCode Available
Efficient Contrastive Explanations on Demand	Dec 24, 2024	Adversarial Robustness	CodeCode Available
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation	Dec 9, 2023	Adversarial RobustnessFairness	CodeCode Available
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness	Dec 1, 2021	Adversarial Robustness	CodeCode Available
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks	Jul 17, 2024	Adversarial RobustnessBenchmarking	CodeCode Available
Effective and Efficient Vote Attack on Capsule Networks	Feb 19, 2021	Adversarial Robustness	CodeCode Available
Understanding the Impact of Adversarial Robustness on Accuracy Disparity	Nov 28, 2022	Adversarial RobustnessOpen-Ended Question Answering	CodeCode Available
Towards Practical Control of Singular Values of Convolutional Layers	Nov 24, 2022	Adversarial Robustness	CodeCode Available
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations	Aug 27, 2024	Adversarial RobustnessAndroid Malware Detection	CodeCode Available
Improving Robustness with Adaptive Weight Decay	Sep 30, 2022	Adversarial Robustness	CodeCode Available
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge	Apr 10, 2025	Adversarial RobustnessBenchmarking	CodeCode Available
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics	Jun 2, 2023	Adversarial Robustness	CodeCode Available
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation	Dec 12, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples	Sep 13, 2017	Adversarial AttackAdversarial Robustness	CodeCode Available
Dynamic Time Warping based Adversarial Framework for Time-Series Domain	Jul 9, 2022	Adversarial RobustnessDynamic Time Warping	CodeCode Available
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks	Nov 29, 2022	Adversarial RobustnessGPU	CodeCode Available
Verifying Properties of Tsetlin Machines	Mar 25, 2023	Adversarial RobustnessInterpretable Machine Learning	CodeCode Available
Do Perceptually Aligned Gradients Imply Adversarial Robustness?	Jul 22, 2022	Adversarial RobustnessImage Classification	CodeCode Available
Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers	Aug 24, 2023	Adversarial Robustnessimage-classification	CodeCode Available
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models	Mar 1, 2020	Adversarial Robustness	CodeCode Available

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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