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 1521–1530 of 1808 papers

TitleStatusHype
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
Boosting Adversarial Transferability of MLP-Mixer—0
Boosting Adversarial Transferability through Enhanced Momentum—0
Boosting Adversarial Transferability using Dynamic Cues—0
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization—0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—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