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 251–300 of 1808 papers

TitleStatusHype
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Towards Resistant Audio Adversarial ExamplesCode1
Towards Transferable Targeted 3D Adversarial Attack in the Physical WorldCode1
Towards Transferable Unrestricted Adversarial Examples with Minimum ChangesCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Boosting the Transferability of Video Adversarial Examples via Temporal TranslationCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial AttackCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
Fluent dreaming for language modelsCode1
A Survey On Universal Adversarial AttackCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
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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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