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 1751–1800 of 1808 papers

TitleStatusHype
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Deep generative models as an adversarial attack strategy for tabular machine learningCode0
DeepFool: a simple and accurate method to fool deep neural networksCode0
A Theoretical View of Linear Backpropagation and Its ConvergenceCode0
Training Meta-Surrogate Model for Transferable Adversarial AttackCode0
On Detecting Adversarial PerturbationsCode0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGACode0
Safety Verification of Deep Neural NetworksCode0
A Targeted Universal Attack on Graph Convolutional NetworkCode0
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and DefensesCode0
Saliency Attack: Towards Imperceptible Black-box Adversarial AttackCode0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
Decorrelative Network Architecture for Robust Electrocardiogram ClassificationCode0
Decision-based Universal Adversarial AttackCode0
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient EstimationCode0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
Data-Driven Subsampling in the Presence of an Adversarial ActorCode0
Trainwreck: A damaging adversarial attack on image classifiersCode0
Sample Attackability in Natural Language Adversarial AttacksCode0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement LearningCode0
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language ModelsCode0
On Robustness of Neural Ordinary Differential EquationsCode0
Data-Driven Falsification of Cyber-Physical SystemsCode0
Text Processing Like Humans Do: Visually Attacking and Shielding NLP SystemsCode0
DAmageNet: A Universal Adversarial DatasetCode0
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting MethodCode0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM InversionCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
Scaleable input gradient regularization for adversarial robustnessCode0
Explain2Attack: Text Adversarial Attacks via Cross-Domain InterpretabilityCode0
On the Perils of Cascading Robust ClassifiersCode0
An Analysis of Robustness of Non-Lipschitz NetworksCode0
Transferable 3D Adversarial Shape Completion using Diffusion ModelsCode0
The Adversarial Attack and Detection under the Fisher Information MetricCode0
Adversarial Attacks on Gaussian Process BanditsCode0
Curls & Whey: Boosting Black-Box Adversarial AttacksCode0
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacksCode0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color AttackCode0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural NetworksCode0
Scratch that! An Evolution-based Adversarial Attack against Neural NetworksCode0
CT-GAT: Cross-Task Generative Adversarial Attack based on TransferabilityCode0
Universalization of any adversarial attack using very few test examplesCode0
Query Attack via Opposite-Direction Feature:Towards Robust Image RetrievalCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition modelsCode0
Certified Adversarial Robustness with Additive NoiseCode0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
Second-Order NLP Adversarial ExamplesCode0
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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