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 626–650 of 1746 papers

Title	Date	Tasks	Status	Hype
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models	May 29, 2023	Adversarial RobustnessDecision Making	—Unverified	0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study	May 28, 2023	Adversarial RobustnessBackdoor Attack	—Unverified	0
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection	May 27, 2023	Adversarial Robustness	—Unverified	0
On the Importance of Backbone to the Adversarial Robustness of Object Detectors	May 27, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available	0
On Evaluating Adversarial Robustness of Large Vision-Language Models	May 26, 2023	Adversarial Robustnessmultimodal generation	CodeCode Available	2
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness	May 25, 2023	Adversarial DefenseAdversarial Purification	CodeCode Available	0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text	May 25, 2023	Adversarial RobustnessClassification	—Unverified	0
IDEA: Invariant Defense for Graph Adversarial Robustness	May 25, 2023	Adversarial Robustness	—Unverified	0
Robust Classification via a Single Diffusion Model	May 24, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness	May 24, 2023	Adversarial RobustnessKnowledge Distillation	—Unverified	0
Non-adversarial Robustness of Deep Learning Methods for Computer Vision	May 24, 2023	Adversarial RobustnessDeep Learning	—Unverified	0
Adversarial robustness of amortized Bayesian inference	May 24, 2023	Adversarial RobustnessBayesian Inference	CodeCode Available	0
Expressive Losses for Verified Robustness via Convex Combinations	May 23, 2023	Adversarial Robustness	CodeCode Available	0
Decoupled Kullback-Leibler Divergence Loss	May 23, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation	May 22, 2023	Adversarial RobustnessFairness	CodeCode Available	0
Annealing Self-Distillation Rectification Improves Adversarial Training	May 20, 2023	Adversarial Robustness	CodeCode Available	0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend	May 18, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning	May 18, 2023	Adversarial RobustnessData Poisoning	CodeCode Available	3
Raising the Bar for Certified Adversarial Robustness with Diffusion Models	May 17, 2023	Adversarial Robustness	—Unverified	0
Variational Classification	May 17, 2023	Adversarial RobustnessClassification	CodeCode Available	0
Iterative Adversarial Attack on Image-guided Story Ending Generation	May 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation	May 16, 2023	Adversarial DefenseAdversarial Robustness	—Unverified	0
Watermarking Text Generated by Black-Box Language Models	May 14, 2023	Adversarial RobustnessLanguage Modelling	CodeCode Available	1
Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI	May 13, 2023	Adversarial Robustness	—Unverified	0
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications	May 12, 2023	Adversarial RobustnessDeep Learning	—Unverified	0

Show:10 25 50

← PrevPage 26 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