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 17761800 of 1808 papers

TitleStatusHype
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified