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 51–100 of 1808 papers

TitleStatusHype
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Adversarial Training for Free!Code1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Adversarial Self-Supervised Contrastive LearningCode1
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords SubstitutionCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
A Survey On Universal Adversarial AttackCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
R&R: Metric-guided Adversarial Sentence GenerationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Adversarial Attack and Defense in Deep RankingCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution LearningCode1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Data-free Universal Adversarial Perturbation with Pseudo-semantic PriorCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
Fooling the Image Dehazing Models by First Order GradientCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
BayesOpt Adversarial AttackCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
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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