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

Showing 16761700 of 1808 papers

TitleStatusHype
A Formalization of Robustness for Deep Neural Networks0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
Attribution-driven Causal Analysis for Detection of Adversarial Examples0
Attack Type Agnostic Perceptual Enhancement of Adversarial Images0
Adversarial Out-domain Examples for Generative ModelsCode0
Adversarial Examples on Graph Data: Deep Insights into Attack and DefenseCode0
On the Effectiveness of Low Frequency Perturbations0
Adversarial Attack and Defense on Point Sets0
Robust Decision Trees Against Adversarial ExamplesCode0
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchCode0
There are No Bit Parts for Sign Bits in Black-Box Attacks0
On Evaluating Adversarial RobustnessCode1
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems0
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?Code0
Optimal Attack against Autoregressive Models by Manipulating the Environment0
The Efficacy of SHIELD under Different Threat Models0
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models0
Weighted-Sampling Audio Adversarial Example Attack0
Theoretically Principled Trade-off between Robustness and AccuracyCode1
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
5AdvTraining [madry2018]Attack: PGD2048.44Unverified
6TRADES [zhang2019b]Attack: PGD2045.9Unverified
7XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified