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 226250 of 1808 papers

TitleStatusHype
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking ModelsCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
Adversarial Training for Free!Code1
Patch-wise++ Perturbation for Adversarial Targeted AttacksCode1
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery DetectionCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera SystemsCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Proximal Splitting Adversarial Attack for Semantic SegmentationCode1
Proximal Splitting Adversarial Attacks for Semantic SegmentationCode1
Random Walks for Adversarial MeshesCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural NetworksCode1
Rethinking Image Restoration for Object DetectionCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
BayesOpt Adversarial AttackCode1
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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