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 101–150 of 1808 papers

TitleStatusHype
Adversarial Attack on Large Scale GraphCode1
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless NetworksCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Disentangled Information BottleneckCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
3D Adversarial Attacks Beyond Point CloudCode1
A Survey On Universal Adversarial AttackCode1
R&R: Metric-guided Adversarial Sentence GenerationCode1
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustnessCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Adversarial Learning for Robust Deep ClusteringCode1
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial AttackCode1
Fluent dreaming for language modelsCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object TrackingCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Frequency-driven Imperceptible Adversarial Attack on Semantic SimilarityCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution LearningCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Adversarial Training for Free!Code1
Adversarial Vulnerability of Randomized EnsemblesCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Adversarial Self-Supervised Contrastive LearningCode1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Augmented Lagrangian Adversarial AttacksCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
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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