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 411–420 of 1808 papers

TitleStatusHype
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color AttackCode0
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems—0
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
A Generative Adversarial Attack for Multilingual Text Classifiers—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
Data-Driven Subsampling in the Presence of an Adversarial ActorCode0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—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