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Adversarial Robustness

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

Papers

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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
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified