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 151–160 of 1808 papers

TitleStatusHype
Robustness-aware Automatic Prompt OptimizationCode0
Retention Score: Quantifying Jailbreak Risks for Vision Language Models—0
Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters—0
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models—0
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation—0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation—0
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera SystemsCode1
Adversarially robust generalization theory via Jacobian regularization for deep neural networks—0
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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