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 1251–1275 of 1808 papers

TitleStatusHype
Sparse and Imperceptible Adversarial Attack via a Homotopy AlgorithmCode0
Transferable Adversarial Examples for Anchor Free Object Detection—0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial AttackCode0
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial AttackCode0
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance SacrificeCode0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness—0
Reducing DNN Properties to Enable Falsification with Adversarial AttacksCode0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Local Aggressive Adversarial Attacks on 3D Point CloudCode0
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
Automated Decision-based Adversarial Attacks—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
AdvHaze: Adversarial Haze Attack—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
Learning Transferable 3D Adversarial Cloaks for Deep Trained DetectorsCode0
Robust Certification for Laplace Learning on Geometric Graphs—0
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions—0
Adversarial Diffusion Attacks on Graph-based Traffic Prediction ModelsCode0
Show:102550
← PrevPage 51 of 73Next →
All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified