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 1626–1650 of 1808 papers

TitleStatusHype
Affine Disentangled GAN for Interpretable and Robust AV Perception—0
Adversarial Attacks in Sound Event Classification—0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary AttackCode0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"—0
Generating Natural Language Adversarial Examples through Probability Weighted Word SaliencyCode0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box AttacksCode0
Adversarial Attack Generation Empowered by Min-Max OptimizationCode0
Robustness for Non-Parametric Classification: A Generic Attack and DefenseCode0
Efficient Project Gradient Descent for Ensemble Adversarial AttackCode0
Query-efficient Meta Attack to Deep Neural NetworksCode0
Should Adversarial Attacks Use Pixel p-Norm?—0
Architecture Selection via the Trade-off Between Accuracy and Robustness—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and DefensesCode0
Improving VAEs' Robustness to Adversarial Attack—0
Enhancing Transformation-based Defenses using a Distribution Classifier—0
Feature Space Perturbations Yield More Transferable Adversarial ExamplesCode0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial RobustnessCode0
Real-Time Adversarial AttacksCode0
Bandlimiting Neural Networks Against Adversarial Attacks—0
Identifying Classes Susceptible to Adversarial Attacks—0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness—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