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 1701–1725 of 1808 papers

TitleStatusHype
Trust Region Based Adversarial Attack on Neural NetworksCode0
Learning Transferable Adversarial Examples via Ghost NetworksCode0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack—0
Prior Networks for Detection of Adversarial Attacks—0
Fooling Network Interpretation in Image Classification—0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous ApplicationsCode0
FineFool: Fine Object Contour Attack via Attention—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
A Frank-Wolfe Framework for Efficient and Effective Adversarial AttacksCode0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust AccuraciesCode0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Task-generalizable Adversarial Attack based on Perceptual MetricCode0
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial AttackCode0
Intermediate Level Adversarial Attack for Enhanced Transferability—0
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding—0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networksCode0
CAAD 2018: Iterative Ensemble Adversarial Attack—0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning—0
Learning to Defend by Learning to Attack—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Improved Network Robustness with Adversary CriticCode0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified