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 1651–1700 of 1808 papers

TitleStatusHype
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain—0
CharBot: A Simple and Effective Method for Evading DGA Classifiers—0
Weight Map Layer for Noise and Adversarial Attack Robustness—0
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm—0
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural NetworksCode0
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the WildCode0
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK—0
Second-Order Adversarial Attack and Certifiable Robustness—0
Adversarial Training for Free!Code1
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping—0
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness—0
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural NetworksCode0
Defensive Quantization: When Efficiency Meets Robustness—0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples—0
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense—0
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
Learning to Defense by Learning to Attack—0
Text Processing Like Humans Do: Visually Attacking and Shielding NLP SystemsCode0
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacksCode0
Defending against Whitebox Adversarial Attacks via Randomized DiscretizationCode0
The LogBarrier adversarial attack: making effective use of decision boundary informationCode0
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
On the Effectiveness of Low Frequency Perturbations—0
Adversarial Attack and Defense on Point Sets—0
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 Attacks—0
On Evaluating Adversarial RobustnessCode1
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
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Theoretically Principled Trade-off between Robustness and AccuracyCode1
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors—0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—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