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 261–270 of 1808 papers

TitleStatusHype
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability DetectionCode1
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous DrivingCode1
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift—0
Compressed models are NOT miniature versions of large models—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent InfectionCode0
AEMIM: Adversarial Examples Meet Masked Image Modeling—0
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical AnalysisCode0
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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