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 1226–1250 of 1746 papers

Title	Date	Tasks	Status
Playing it Safe: Adversarial Robustness with an Abstain Option	Nov 25, 2019	Adversarial Robustness	—Unverified
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models	Sep 29, 2021	Adversarial Robustnessimage-classification	—Unverified
Poisoning Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks	Dec 9, 2023	Adversarial Robustness	—Unverified
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation	Jan 26, 2023	Adversarial RobustnessMuJoCo	—Unverified
Policy Smoothing for Provably Robust Reinforcement Learning	Jun 21, 2021	Adversarial Robustnessimage-classification	—Unverified
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation	Jan 28, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism	Nov 21, 2022	Adversarial RobustnessAutonomous Driving	—Unverified
Power up! Robust Graph Convolutional Network based on Graph Powering	Sep 25, 2019	Adversarial Robustness	—Unverified
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training	May 25, 2021	Adversarial RobustnessBinary Classification	—Unverified
Adversarial Robustness on Image Classification with k-means	Dec 15, 2023	Adversarial RobustnessClassification	—Unverified
Adversarial Robustness of Visual Dialog	Jul 6, 2022	Adversarial RobustnessVisual Dialog	—Unverified
A case for new neural networks smoothness constraints	Oct 19, 2020	Adversarial RobustnessInductive Bias	—Unverified
Adversarial Robustness of Streaming Algorithms through Importance Sampling	Jun 28, 2021	Adversarial RobustnessClustering	—Unverified
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning	Jan 25, 2025	Adversarial RobustnessFederated Learning	—Unverified
Adversarial Purification with the Manifold Hypothesis	Oct 26, 2022	Adversarial PurificationAdversarial Robustness	—Unverified
Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers	Mar 21, 2025	Adversarial RobustnessFairness	—Unverified
PRISON: Unmasking the Criminal Potential of Large Language Models	Jun 19, 2025	Adversarial Robustness	—Unverified
Adaptive Batch Normalization Networks for Adversarial Robustness	May 20, 2024	Adversarial DefenseAdversarial Robustness	—Unverified
Adversarial robustness of sparse local Lipschitz predictors	Feb 26, 2022	Adversarial Robustness	—Unverified
Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide	Feb 20, 2025	Adversarial RobustnessBenchmarking	—Unverified
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach	Aug 24, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples	Jun 19, 2020	Adversarial RobustnessVocal Bursts Intensity Prediction	—Unverified
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches	Oct 11, 2023	Adversarial Robustness	—Unverified
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection	Jun 2, 2022	Adversarial RobustnessImage Segmentation	—Unverified
Proper Measure for Adversarial Robustness	Sep 28, 2020	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