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 276300 of 1808 papers

TitleStatusHype
R&R: Metric-guided Adversarial Sentence GenerationCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language ModelsCode1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
Augmented Lagrangian Adversarial AttacksCode1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Guided Adversarial Attack for Evaluating and Enhancing Adversarial DefensesCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
3D Adversarial Attacks Beyond Point CloudCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text AttacksCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object TrackingCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan ScriptCode1
Motion-Excited Sampler: Video Adversarial Attack with Sparked PriorCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Improving Adversarial Transferability with Gradient RefiningCode1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness VerificationCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object TrackingCode1
Pick-Object-Attack: Type-Specific Adversarial Attack for Object DetectionCode1
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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