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 1471–1480 of 1808 papers

TitleStatusHype
Evaluations and Methods for Explanation through Robustness Analysis—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning—0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
Universalization of any adversarial attack using very few test examplesCode0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—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