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 171–180 of 1808 papers

TitleStatusHype
Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies—0
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?—0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks—0
Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan ScriptCode0
Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language ModelCode0
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face DetectionCode1
Intermediate Outputs Are More Sensitive Than You Think—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Scaling Laws for Black box Adversarial Attacks—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