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 1191–1200 of 1808 papers

TitleStatusHype
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense—0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks—0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Optical Adversarial Attack—0
Optimal Attack against Autoregressive Models by Manipulating the Environment—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