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 1676–1700 of 1808 papers

TitleStatusHype
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Towards Analyzing Semantic Robustness of Deep Neural NetworksCode0
HopSkipJumpAttack: A Query-Efficient Decision-Based AttackCode0
Curls & Whey: Boosting Black-Box Adversarial AttacksCode0
Adversarial Attacks against Deep Saliency Models—0
Text Processing Like Humans Do: Visually Attacking and Shielding NLP SystemsCode0
Learning to Defense by Learning to Attack—0
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacksCode0
The LogBarrier adversarial attack: making effective use of decision boundary informationCode0
Defending against Whitebox Adversarial Attacks via Randomized DiscretizationCode0
A Formalization of Robustness for Deep Neural Networks—0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
Attribution-driven Causal Analysis for Detection of Adversarial Examples—0
Attack Type Agnostic Perceptual Enhancement of Adversarial Images—0
Adversarial Out-domain Examples for Generative ModelsCode0
Adversarial Examples on Graph Data: Deep Insights into Attack and DefenseCode0
Adversarial Attack and Defense on Point Sets—0
On the Effectiveness of Low Frequency Perturbations—0
Robust Decision Trees Against Adversarial ExamplesCode0
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchCode0
There are No Bit Parts for Sign Bits in Black-Box Attacks—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?Code0
Optimal Attack against Autoregressive Models by Manipulating the Environment—0
The Efficacy of SHIELD under Different Threat Models—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