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 1526–1550 of 1746 papers

Title	Date	Tasks	Status
Do Wider Neural Networks Really Help Adversarial Robustness?	Oct 3, 2020	Adversarial Robustness	—Unverified
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?	May 4, 2020	Adversarial RobustnessAndroid Malware Detection	—Unverified
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability	Jan 1, 2020	Adversarial Robustness	—Unverified
Don't let your Discriminator be fooled	May 1, 2019	Adversarial Robustness	—Unverified
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation	Oct 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text	May 25, 2023	Adversarial RobustnessClassification	—Unverified
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency	Sep 11, 2021	Adversarial RobustnessQuantization	—Unverified
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness	Jan 16, 2025	Adversarial DefenseAdversarial Robustness	—Unverified
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—Unverified
Can Attention Masks Improve Adversarial Robustness?	Nov 27, 2019	Adversarial Robustness	—Unverified
Dropping Pixels for Adversarial Robustness	May 1, 2019	Adversarial Robustness	—Unverified
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks	Sep 5, 2020	Adversarial Robustness	—Unverified
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks	May 12, 2025	Adversarial RobustnessLow-rank compression	—Unverified
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model	May 6, 2021	Adversarial RobustnessAttribute	—Unverified
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness	Aug 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training	Jun 3, 2025	Adversarial RobustnessScheduling	—Unverified
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness	Aug 23, 2021	Adversarial RobustnessDenoising	—Unverified
Adversarially Robust Neural Architectures	Sep 2, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Edge-Only Universal Adversarial Attacks in Distributed Learning	Nov 15, 2024	Adversarial Robustness	—Unverified
Self-Knowledge Distillation via Dropout	Aug 11, 2022	Adversarial Robustnessimage-classification	—Unverified
Effective, Efficient and Robust Neural Architecture Search	Nov 19, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified
Effects of Loss Functions And Target Representations on Adversarial Robustness	Dec 1, 2018	Adversarial Robustness	—Unverified
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks	May 9, 2021	Adversarial Robustness	—Unverified
Intrinsic Biologically Plausible Adversarial Robustness	Sep 29, 2023	Adversarial Robustness	—Unverified
Efficient Certification for Probabilistic Robustness	Sep 29, 2021	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