SOTAVerified

Reconstruction Attack

Facial reconstruction attack of facial manipulation models such as: Face swapping models, anonymization models, etc.

Papers

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Showing 1–25 of 68 papers

TitleStatusHype
Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and ReconstructionCode2
Reconstructing Training Data with Informed AdversariesCode1
Trained Random Forests Completely Reveal your DatasetCode1
Inference Attacks Against Graph Neural NetworksCode1
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT ModelsCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain ApproximationCode1
Confidence-Ranked Reconstruction of Census Microdata from Published StatisticsCode1
Bayes' capacity as a measure for reconstruction attacks in federated learning—0
Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning—0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning—0
Hidden Data Privacy Breaches in Federated Learning—0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning—0
Cloud-based Federated Boosting for Mobile Crowdsensing—0
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates—0
Approximate and Weighted Data Reconstruction Attack in Federated Learning—0
Defending against Reconstruction Attack in Vertical Federated Learning—0
Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models—0
Differentially Private Instance Encoding against Privacy Attacks—0
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?—0
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences—0
Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model—0
Bounding Training Data Reconstruction in DP-SGD—0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning—0
Bounding Reconstruction Attack Success of Adversaries Without Data Priors—0
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