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 351–360 of 1808 papers

TitleStatusHype
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection—0
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous DrivingCode2
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language ModelsCode0
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking—0
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMsCode0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—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