SOTAVerified

Reconstruction Attack

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

Papers

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Showing 26–50 of 68 papers

TitleStatusHype
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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