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 1731–1740 of 1808 papers

TitleStatusHype
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
Dynamic backdoor attacks against federated learning—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
STA: Adversarial Attacks on Siamese Trackers—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Stabilized Medical Attacks—0
A Bayes-Optimal View on Adversarial Examples—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