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 1201–1225 of 1746 papers

Title	Date	Tasks	Status
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks	Apr 30, 2024	Adversarial Robustness	—Unverified
Parameterizing Activation Functions for Adversarial Robustness	Oct 11, 2021	Adversarial Robustness	—Unverified
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness	Nov 3, 2021	Adversarial RobustnessSensitivity	—Unverified
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified
Partially Recentralization Softmax Loss for Vision-Language Models Robustness	Feb 6, 2024	Adversarial RobustnessDiversity	—Unverified
SOAR: Second-Order Adversarial Regularization	Apr 4, 2020	Adversarial Robustness	—Unverified
Adversarial Robustness through Local Linearization	Jul 4, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
P-CapsNets: a General Form of Convolutional Neural Networks	Dec 18, 2019	Adversarial RobustnessForm	—Unverified
An Empirical Evaluation of Adversarial Robustness under Transfer Learning	May 7, 2019	Adversarial RobustnessTransfer Learning	—Unverified
Adversarial Robustness through Dynamic Ensemble Learning	Dec 20, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Perception Over Time: Temporal Dynamics for Robust Image Understanding	Mar 11, 2022	Adversarial Robustness	—Unverified
Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models	Jan 1, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Adversarial Robustness Through Artifact Design	Feb 7, 2024	Adversarial RobustnessTraffic Sign Recognition	—Unverified
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems	Jul 12, 2021	Adversarial RobustnessAutomatic Speech Recognition	—Unverified
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation	Sep 2, 2020	Adversarial RobustnessSuper-Resolution	—Unverified
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation	Aug 7, 2024	Adversarial RobustnessImage Segmentation	—Unverified
Perlin Noise Improve Adversarial Robustness	Dec 26, 2021	Adversarial Robustness	—Unverified
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off	Dec 16, 2023	Adversarial RobustnessInformation Retrieval	—Unverified
Perturbation Type Categorization for Multiple _p Bounded Adversarial Robustness	Jan 1, 2021	Adversarial RobustnessVocal Bursts Type Prediction	—Unverified
Adversarial Robustness: Softmax versus Openmax	Aug 5, 2017	Adversarial RobustnessOpen Set Learning	—Unverified
Phase-shifted Adversarial Training	Jan 12, 2023	Adversarial Robustness	—Unverified
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications	May 12, 2023	Adversarial RobustnessDeep Learning	—Unverified
Visually Adversarial Attacks and Defenses in the Physical World: A Survey	Nov 3, 2022	Adversarial RobustnessSurvey	—Unverified
Adversarial Robustness Overestimation and Instability in TRADES	Oct 10, 2024	Adversarial Robustness	—Unverified
Planting Undetectable Backdoors in Machine Learning Models	Apr 14, 2022	Adversarial RobustnessBIG-bench Machine Learning	—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