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 801–825 of 1746 papers

Title	Date	Tasks	Status
Achieving Adversarial Robustness Requires An Active Teacher	Dec 14, 2020	Adversarial Robustness	—Unverified
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness	Jun 17, 2024	Adversarial Robustness	—Unverified
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing	Aug 23, 2019	Adversarial Robustness	—Unverified
Are models trained on temporally-continuous data streams more adversarially robust?	Oct 12, 2021	Adversarial Robustness	—Unverified
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework	Nov 22, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness	Jan 1, 2021	Adversarial Robustness	—Unverified
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons	Jan 31, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Efficient Certification for Probabilistic Robustness	Sep 29, 2021	Adversarial Robustness	—Unverified
Hierarchical Verification for Adversarial Robustness	Jul 23, 2020	Adversarial Robustness	—Unverified
A3T: Adversarially Augmented Adversarial Training	Jan 12, 2018	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Jun 15, 2020	Adversarial RobustnessData Augmentation	—Unverified
Holistic Adversarially Robust Pruning	Dec 19, 2024	Adversarial Robustness	—Unverified
Holistic Adversarial Robustness of Deep Learning Models	Feb 15, 2022	Adversarial RobustnessDeep Learning	—Unverified
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction	Mar 16, 2024	Adversarial RobustnessImage-text Retrieval	—Unverified
Intrinsic Biologically Plausible Adversarial Robustness	Sep 29, 2023	Adversarial Robustness	—Unverified
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks	May 9, 2021	Adversarial Robustness	—Unverified
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning	Jun 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks	Aug 7, 2022	Adversarial RobustnessRobust classification	—Unverified
How and When Adversarial Robustness Transfers in Knowledge Distillation?	Oct 22, 2021	Adversarial RobustnessKnowledge Distillation	—Unverified
How benign is benign overfitting?	Jul 8, 2020	Adversarial RobustnessRepresentation Learning	—Unverified
How Benign is Benign Overfitting ?	Jan 1, 2021	Adversarial RobustnessRepresentation Learning	—Unverified
How Do Diffusion Models Improve Adversarial Robustness?	May 28, 2025	Adversarial PurificationAdversarial Robustness	—Unverified
How do SGD hyperparameters in natural training affect adversarial robustness?	Jun 20, 2020	Adversarial Robustness	—Unverified
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection	Nov 8, 2022	Adversarial RobustnessRecommendation Systems	—Unverified
Effects of Loss Functions And Target Representations on Adversarial Robustness	Dec 1, 2018	Adversarial Robustness	—Unverified

Show:10 25 50

← PrevPage 33 of 70Next →

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