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 1401–1425 of 1746 papers

Title	Date	Tasks	Status
Towards Evaluating the Robustness of Neural Networks Learned by Transduction	Oct 27, 2021	Adversarial RobustnessBilevel Optimization	CodeCode Available
Physics-constrained Attack against Convolution-based Human Motion Prediction	Jun 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing	Jun 10, 2023	Adversarial RobustnessAutonomous Navigation	CodeCode Available
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness	Apr 23, 2020	Adversarial Robustness	CodeCode Available
Evading classifiers in discrete domains with provable optimality guarantees	Oct 25, 2018	Adversarial RobustnessSpam detection	CodeCode Available
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming	Jun 19, 2023	Adversarial RobustnessComputational Efficiency	CodeCode Available
Prediction without Preclusion: Recourse Verification with Reachable Sets	Aug 24, 2023	Adversarial RobustnessPrediction	CodeCode Available
Biologically Inspired Mechanisms for Adversarial Robustness	Jun 29, 2020	Adversarial Robustness	CodeCode Available
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning	Aug 9, 2023	Adversarial Robustness	CodeCode Available
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks	Nov 3, 2019	Adversarial Robustness	CodeCode Available
How to compare adversarial robustness of classifiers from a global perspective	Apr 22, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks	Nov 20, 2024	Adversarial Robustnessimage-classification	CodeCode Available
Error Diffusion Halftoning Against Adversarial Examples	Jan 23, 2021	Adversarial RobustnessQuantization	CodeCode Available
ProARD: progressive adversarial robustness distillation: provide wide range of robust students	Jun 9, 2025	Adversarial Robustness	CodeCode Available
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks	Dec 1, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck	Mar 24, 2023	Adversarial RobustnessNovelty Detection	CodeCode Available
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense	Feb 2, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available
Adversarial Machine Learning in Latent Representations of Neural Networks	Sep 29, 2023	Adversarial RobustnessEdge-computing	CodeCode Available
Enhancing Adversarial Training via Reweighting Optimization Trajectory	Jun 25, 2023	Adversarial Robustness	CodeCode Available
Adversarial Robustness via Fisher-Rao Regularization	Jun 12, 2021	Adversarial DefenseAdversarial Robustness	CodeCode Available
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability	Jun 9, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises	Jun 15, 2022	Adversarial RobustnessFoveation	CodeCode Available
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation	Aug 8, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach	Sep 19, 2024	Adversarial Robustnessobject-detection	CodeCode Available
Projected Randomized Smoothing for Certified Adversarial Robustness	Sep 25, 2023	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