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 901–925 of 1746 papers

Title	Date	Tasks	Status
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection	Mar 27, 2023	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available
CAT:Collaborative Adversarial Training	Mar 27, 2023	Adversarial Robustness	CodeCode Available
Verifying Properties of Tsetlin Machines	Mar 25, 2023	Adversarial RobustnessInterpretable Machine Learning	CodeCode Available
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck	Mar 24, 2023	Adversarial RobustnessNovelty Detection	CodeCode Available
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
Optimization and Optimizers for Adversarial Robustness	Mar 23, 2023	Adversarial Robustness	—Unverified
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection	Mar 23, 2023	Adversarial RobustnessHeart Rate Variability	—Unverified
Revisiting DeepFool: generalization and improvement	Mar 22, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval	Mar 22, 2023	Adversarial RobustnessDeep Hashing	—Unverified
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation	Mar 21, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense	Mar 21, 2023	Adversarial AttackAdversarial Defense	—Unverified
GNN-Ensemble: Towards Random Decision Graph Neural Networks	Mar 20, 2023	Adversarial RobustnessDecision Making	—Unverified
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness	Mar 17, 2023	Adversarial RobustnessAll	—Unverified
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations	Mar 15, 2023	Adversarial Robustness	—Unverified
Model-tuning Via Prompts Makes NLP Models Adversarially Robust	Mar 13, 2023	Adversarial RobustnessLanguage Modelling	CodeCode Available
Robustness-preserving Lifelong Learning via Dataset Condensation	Mar 7, 2023	Adversarial RobustnessDataset Condensation	—Unverified
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes	Mar 4, 2023	Adversarial RobustnessDiversity	CodeCode Available
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms	Mar 3, 2023	Adversarial RobustnessModel Compression	—Unverified
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks	Mar 2, 2023	Adversarial Robustness	—Unverified
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking	Feb 28, 2023	Adversarial RobustnessBenchmarking	—Unverified
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases	Feb 28, 2023	Adversarial RobustnessNatural Language Inference	—Unverified
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators	Feb 27, 2023	Adversarial Robustness	—Unverified
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective	Feb 22, 2023	Adversarial RobustnessChatbot	—Unverified
Delving into the Adversarial Robustness of Federated Learning	Feb 19, 2023	Adversarial RobustnessFederated Learning	—Unverified
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition	Feb 17, 2023	Adversarial RobustnessFairness	—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