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 1726–1746 of 1746 papers

Title	Date	Tasks	Status
Generalizability of Adversarial Robustness Under Distribution Shifts	Sep 29, 2022	Adversarial RobustnessDomain Generalization	—Unverified
Generalizable Deepfake Detection with Phase-Based Motion Analysis	Nov 17, 2022	Adversarial RobustnessDeepFake Detection	—Unverified
Generalization Certificates for Adversarially Robust Bayesian Linear Regression	Feb 20, 2025	Adversarial RobustnessBayesian Inference	—Unverified
Generalization Error Analysis of Neural networks with Gradient Based Regularization	Jul 6, 2021	Adversarial Robustnessimage-classification	—Unverified
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness	Oct 21, 2021	Adversarial RobustnessCombinatorial Optimization	—Unverified
Incorporating Hidden Layer representation into Adversarial Attacks and Defences	Nov 28, 2020	Adversarial Robustness	—Unverified
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness	Mar 15, 2022	Adversarial RobustnessData Augmentation	—Unverified
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework	Oct 3, 2023	Adversarial RobustnessScheduling	—Unverified
Generalizing and Improving Jacobian and Hessian Regularization	Dec 1, 2022	Adversarial Robustness	—Unverified
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems	Dec 16, 2020	Adversarial Robustness	—Unverified
A Useful Taxonomy for Adversarial Robustness of Neural Networks	Oct 23, 2019	Adversarial Robustness	—Unverified
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers	Dec 30, 2021	Adversarial RobustnessTransfer Learning	—Unverified
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Generating Structured Adversarial Attacks Using Frank-Wolfe Method	Feb 15, 2021	Adversarial Robustness	—Unverified
Adversarial Examples on Segmentation Models Can be Easy to Transfer	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified
GenFighter: A Generative and Evolutive Textual Attack Removal	Apr 17, 2024	Adversarial RobustnessTransfer Learning	—Unverified
GenLabel: Mixup Relabeling using Generative Models	Jan 7, 2022	Adversarial RobustnessData Augmentation	—Unverified
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing	Dec 3, 2024	Adversarial RobustnessData Augmentation	—Unverified
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent	Mar 25, 2025	Adversarial Robustness	—Unverified
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks	Aug 26, 2022	Adversarial RobustnessParameter Prediction	—Unverified
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection	Feb 15, 2022	Adversarial RobustnessMalware Detection	—Unverified

Show:10 25 50

← PrevPage 70 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