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

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Showing 611–620 of 1808 papers

TitleStatusHype
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack PerspectiveCode0
A General Black-box Adversarial Attack on Graph-based Fake News Detectors—0
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory predictionCode0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency—0
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of ArtifactsCode0
Towards Building a Robust Toxicity Predictor—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