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 776–800 of 1746 papers

Title	Date	Tasks	Status
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems	Dec 16, 2020	Adversarial Robustness	—Unverified
Improve Adversarial Robustness via Weight Penalization on Classification Layer	Oct 8, 2020	Adversarial RobustnessClassification	—Unverified
SOAR: Second-Order Adversarial Regularization	Apr 4, 2020	Adversarial Robustness	—Unverified
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks	Nov 3, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Generating Structured Adversarial Attacks Using Frank-Wolfe Method	Feb 15, 2021	Adversarial Robustness	—Unverified
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness	Feb 11, 2022	Adversarial Robustness	—Unverified
GenFighter: A Generative and Evolutive Textual Attack Removal	Apr 17, 2024	Adversarial RobustnessTransfer Learning	—Unverified
Eight challenges in developing theory of intelligence	Jun 20, 2023	Adversarial RobustnessContinual Learning	—Unverified
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing	Dec 3, 2024	Adversarial RobustnessData Augmentation	—Unverified
Training Graph Neural Networks Using Non-Robust Samples	Dec 19, 2024	Adversarial RobustnessGraph Neural Network	—Unverified
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks	Aug 26, 2022	Adversarial RobustnessParameter Prediction	—Unverified
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?	Oct 18, 2019	Adversarial Robustness	—Unverified
Global Adversarial Robustness Guarantees for Neural Networks	Sep 25, 2019	Adversarial Robustness	—Unverified
Bridged Adversarial Training	Aug 25, 2021	Adversarial Robustness	—Unverified
GNN-Ensemble: Towards Random Decision Graph Neural Networks	Mar 20, 2023	Adversarial RobustnessDecision Making	—Unverified
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field	Dec 26, 2024	Adversarial RobustnessAttribute	—Unverified
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization	Jul 6, 2021	Adversarial RobustnessDiversity	—Unverified
Are models trained on temporally-continuous data streams more adversarially robust?	Oct 12, 2021	Adversarial Robustness	—Unverified
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks	Sep 27, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified
GridMix: Strong regularization through local context mapping	Jan 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks	Dec 11, 2024	Adversarial RobustnessClassification	—Unverified
Guess First to Enable Better Compression and Adversarial Robustness	Jan 10, 2020	Adversarial Robustness	—Unverified
Guidance Through Surrogate: Towards a Generic Diagnostic Attack	Dec 30, 2022	Adversarial RobustnessDiagnostic	—Unverified
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons	Jan 31, 2022	Adversarial AttackAdversarial 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