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 1351–1375 of 1808 papers

TitleStatusHype
Retention Score: Quantifying Jailbreak Risks for Vision Language Models—0
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Adversarial Attack with Pattern Replacement—0
Rethinking Classifier and Adversarial Attack—0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes—0
Transferable Adversarial Examples for Anchor Free Object Detection—0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
Rethinking Textual Adversarial Defense for Pre-trained Language Models—0
Adaptive Adversarial Attack on Scene Text Recognition—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Reversible Attack based on Local Visual Adversarial Perturbation—0
Reversible Adversarial Attack based on Reversible Image Transformation—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Transferable Perturbations of Deep Feature Distributions—0
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
Transferable Physical Attack against Object Detection with Separable Attention—0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey—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