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 1621–1630 of 1808 papers

TitleStatusHype
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense—0
DA^3: A Distribution-Aware Adversarial Attack against Language Models—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Darknet Traffic Classification and Adversarial Attacks—0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack—0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—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