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Reconstruction Attack

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

Papers

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Showing 1–50 of 68 papers

TitleStatusHype
Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and ReconstructionCode2
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain ApproximationCode1
Inference Attacks Against Graph Neural NetworksCode1
Confidence-Ranked Reconstruction of Census Microdata from Published StatisticsCode1
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT ModelsCode1
Reconstructing Training Data with Informed AdversariesCode1
Trained Random Forests Completely Reveal your DatasetCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
Cloud-based Federated Boosting for Mobile Crowdsensing—0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning—0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation—0
Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models—0
Defending against Reconstruction Attack in Vertical Federated Learning—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
FIVA: Facial Image and Video Anonymization and Anonymization Defense—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
ReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels—0
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis—0
A Novel Attribute Reconstruction Attack in Federated Learning—0
Approximate and Weighted Data Reconstruction Attack in Federated Learning—0
Bayes' capacity as a measure for reconstruction attacks in federated learning—0
Bounding Reconstruction Attack Success of Adversaries Without Data Priors—0
Bounding Training Data Reconstruction in DP-SGD—0
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates—0
The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets—0
On the Reconstruction of Face Images from Deep Face Templates—0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition—0
Privacy Safe Representation Learning via Frequency Filtering Encoder—0
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems—0
Reconstruction Attack on Instance Encoding for Language Understanding—0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
SAPAG: A Self-Adaptive Privacy Attack From Gradients—0
Supervised and Unsupervised Alignments for Spoofing Behavioral Biometrics—0
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis—0
UIFV: Data Reconstruction Attack in Vertical Federated Learning—0
Unveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding—0
Visual Privacy Auditing with Diffusion Models—0
Vulnerability of Face Recognition Systems Against Composite Face Reconstruction Attack—0
VVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution—0
Input Reconstruction Attack against Vertical Federated Large Language Models—0
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model—0
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation—0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy—0
Local Model Reconstruction Attacks in Federated Learning and their Uses—0
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