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 676–700 of 1746 papers

Title	Date	Tasks	Status	Hype
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving	Mar 30, 2023	3D Object DetectionAdversarial Robustness	CodeCode Available	1
Targeted Adversarial Attacks on Wind Power Forecasts	Mar 29, 2023	Adversarial Robustness	CodeCode Available	0
Latent Feature Relation Consistency for Adversarial Robustness	Mar 29, 2023	Adversarial RobustnessRelation	CodeCode Available	0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness	Mar 29, 2023	Adversarial Robustnessimage-classification	—Unverified	0
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion	Mar 29, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition	Mar 28, 2023	Adversarial RobustnessFace Recognition	CodeCode Available	1
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm	Mar 28, 2023	Adversarial RobustnessData Poisoning	—Unverified	0
CAT:Collaborative Adversarial Training	Mar 27, 2023	Adversarial Robustness	CodeCode Available	0
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection	Mar 27, 2023	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	0
CFA: Class-wise Calibrated Fair Adversarial Training	Mar 25, 2023	Adversarial RobustnessFairness	CodeCode Available	1
Verifying Properties of Tsetlin Machines	Mar 25, 2023	Adversarial RobustnessInterpretable Machine Learning	CodeCode Available	0
Feature Separation and Recalibration for Adversarial Robustness	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck	Mar 24, 2023	Adversarial RobustnessNovelty Detection	CodeCode Available	0
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection	Mar 23, 2023	Adversarial RobustnessHeart Rate Variability	—Unverified	0
Optimization and Optimizers for Adversarial Robustness	Mar 23, 2023	Adversarial Robustness	—Unverified	0
Revisiting DeepFool: generalization and improvement	Mar 22, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval	Mar 22, 2023	Adversarial RobustnessDeep Hashing	—Unverified	0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation	Mar 21, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified	0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense	Mar 21, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness	Mar 20, 2023	Adversarial RobustnessMalware Detection	CodeCode Available	1
GNN-Ensemble: Towards Random Decision Graph Neural Networks	Mar 20, 2023	Adversarial RobustnessDecision Making	—Unverified	0
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization	Mar 20, 2023	Adversarial Robustnessimage-classification	CodeCode Available	1
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness	Mar 17, 2023	Adversarial RobustnessAll	—Unverified	0
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified _p Attacks	Mar 17, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1

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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