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

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Showing 1–50 of 1808 papers

TitleStatusHype
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Universal and Transferable Adversarial Attacks on Aligned Language ModelsCode4
Fast Minimum-norm Adversarial Attacks through Adaptive Norm ConstraintsCode2
Adversarial Attacks and Defenses in Images, Graphs and Text: A ReviewCode2
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy ProtectionCode2
A Little Fog for a Large TurnCode2
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language ModelsCode2
Efficient Neural Network Analysis with Sum-of-InfeasibilitiesCode2
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language ModelsCode2
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse AutoencodersCode2
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Diffusion Models for Imperceptible and Transferable Adversarial AttackCode2
Attacking and Defending Machine Learning Applications of Public CloudCode2
Backdoor Learning: A SurveyCode2
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
Fast Adversarial Attacks on Language Models In One GPU MinuteCode2
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial AttackCode2
On Discrete Prompt Optimization for Diffusion ModelsCode2
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical StudiesCode2
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A SurveyCode2
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous DrivingCode2
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal AlignmentCode2
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial AttacksCode2
Foolbox: A Python toolbox to benchmark the robustness of machine learning modelsCode2
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Adversarial Self-Supervised Contrastive LearningCode1
Adversarial Ranking Attack and DefenseCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Training for Free!Code1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Watch out! Motion is Blurring the Vision of Your Deep Neural NetworksCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Data-free Universal Adversarial Perturbation with Pseudo-semantic PriorCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
3D Gaussian Splat VulnerabilitiesCode1
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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