SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 491–500 of 1808 papers

TitleStatusHype
LiDAttack: Robust Black-box Attack on LiDAR-based Object DetectionCode0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
Pseudo-Conversation Injection for LLM Goal Hijacking—0
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Evaluating the Robustness of LiDAR Point Cloud Tracking Against Adversarial Attack—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Adversarial Attacks on Large Language Models Using Regularized RelaxationCode0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language ModelsCode0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified