SOTAVerified

Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 1746 papers

TitleStatusHype
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix ApproachCode0
Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking—0
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models—0
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness AnalysisCode1
PRISON: Unmasking the Criminal Potential of Large Language Models—0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models—0
Canonical Latent Representations in Conditional Diffusion Models—0
Show:102550
← PrevPage 1 of 175Next →
All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified