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 1481–1490 of 1808 papers

TitleStatusHype
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Benchmarking Adversarial Robustness—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Attribution-driven Causal Analysis for Detection of Adversarial Examples—0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Show:102550
← PrevPage 149 of 181Next →
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