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 125 of 1808 papers

TitleStatusHype
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Universal and Transferable Adversarial Attacks on Aligned Language ModelsCode4
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal AlignmentCode2
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse AutoencodersCode2
Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A SurveyCode2
On Discrete Prompt Optimization for Diffusion ModelsCode2
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language ModelsCode2
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy ProtectionCode2
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial AttackCode2
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous DrivingCode2
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language ModelsCode2
Fast Adversarial Attacks on Language Models In One GPU MinuteCode2
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial AttacksCode2
Diffusion Models for Imperceptible and Transferable Adversarial AttackCode2
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Efficient Neural Network Analysis with Sum-of-InfeasibilitiesCode2
Fast Minimum-norm Adversarial Attacks through Adaptive Norm ConstraintsCode2
Attacking and Defending Machine Learning Applications of Public CloudCode2
Backdoor Learning: A SurveyCode2
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical StudiesCode2
A Little Fog for a Large TurnCode2
Adversarial Attacks and Defenses in Images, Graphs and Text: A ReviewCode2
Foolbox: A Python toolbox to benchmark the robustness of machine learning modelsCode2
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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