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 451–475 of 1808 papers

TitleStatusHype
Black-box Adversarial ML Attack on Modulation Classification—0
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
Boosting Adversarial Transferability of MLP-Mixer—0
Boosting Adversarial Transferability through Enhanced Momentum—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Boosting Adversarial Transferability using Dynamic Cues—0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Augmented Adversarial Trigger Learning—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—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