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 1171–1180 of 1808 papers

TitleStatusHype
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based VisionCode0
Reversible Attack based on Local Visual Adversarial Perturbation—0
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
An Improved Genetic Algorithm and Its Application in Neural Network Adversarial AttackCode0
Adversarial defenses via a mixture of generators—0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack—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