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 821–830 of 1808 papers

TitleStatusHype
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
Graph-based methods coupled with specific distributional distances for adversarial attack detectionCode0
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkCode0
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision MakingCode0
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Attribute-Guided Encryption with Facial Texture Masking—0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space—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