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 151–200 of 1808 papers

TitleStatusHype
Black-box Adversarial Example Generation with Normalizing FlowsCode1
Boosting Adversarial Transferability via Gradient Relevance AttackCode1
Adversarial Attack and Defense in Deep RankingCode1
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial PerturbationCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack FrameworkCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Self-Supervised Contrastive LearningCode1
Composite Adversarial AttacksCode1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible NoisesCode1
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security DataCode1
Deep Feature Space Trojan Attack of Neural Networks by Controlled DetoxificationCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless NetworksCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Differentiable JPEG: The Devil is in the DetailsCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural NetworksCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
Efficient Training of Robust Decision Trees Against Adversarial ExamplesCode1
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression RecognitionCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial AttackCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Fluent dreaming for language modelsCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Geometric Adversarial Attacks and Defenses on 3D Point CloudsCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
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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