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 651–675 of 1746 papers

Title	Date	Tasks	Status	Hype
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models	May 12, 2023	2D Object DetectionAdversarial Robustness	CodeCode Available	1
Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications	May 11, 2023	Adversarial Robustness	CodeCode Available	0
Inter-frame Accelerate Attack against Video Interpolation Models	May 11, 2023	Adversarial RobustnessVideo Frame Interpolation	—Unverified	0
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions	May 9, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	0
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness	May 9, 2023	Adversarial RobustnessMathematical Proofs	CodeCode Available	1
Stratified Adversarial Robustness with Rejection	May 2, 2023	Adversarial RobustnessRobust classification	CodeCode Available	0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples	May 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Revisiting Robustness in Graph Machine Learning	May 1, 2023	Adversarial Robustness	—Unverified	0
Lyapunov-Stable Deep Equilibrium Models	Apr 25, 2023	Adversarial DefenseAdversarial Robustness	—Unverified	0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks	Apr 25, 2023	Adversarial RobustnessQuantization	—Unverified	0
Test-Time Adaptation with Perturbation Consistency Learning	Apr 25, 2023	Adversarial RobustnessPseudo Label	—Unverified	0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning	Apr 24, 2023	Adversarial RobustnessTransfer Learning	—Unverified	0
Evaluating Adversarial Robustness on Document Image Classification	Apr 24, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Robust and differentially private stochastic linear bandits	Apr 23, 2023	Adversarial Robustness	—Unverified	0
Individual Fairness in Bayesian Neural Networks	Apr 21, 2023	Adversarial RobustnessBayesian Inference	CodeCode Available	0
Using Z3 for Formal Modeling and Verification of FNN Global Robustness	Apr 20, 2023	Adversarial Robustness	CodeCode Available	0
Certified Adversarial Robustness Within Multiple Perturbation Bounds	Apr 20, 2023	Adversarial Robustness	CodeCode Available	0
Wavelets Beat Monkeys at Adversarial Robustness	Apr 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models	Apr 19, 2023	Adversarial Robustness	CodeCode Available	0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications	Apr 14, 2023	Adversarial Robustness	—Unverified	0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection	Apr 11, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Hyper-parameter Tuning for Adversarially Robust Models	Apr 5, 2023	Adversarial RobustnessGPU	CodeCode Available	0
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks	Apr 4, 2023	Adversarial RobustnessDNN Testing	—Unverified	0
Towards Adversarially Robust Continual Learning	Mar 31, 2023	Adversarial RobustnessContinual Learning	—Unverified	0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	0

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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