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Found 6 papers, 4 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings
The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive information under privacy attacks.
DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask.
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
For instance, studies have revealed that federated learning is vulnerable to backdoor attacks, whereby a compromised participant can stealthily modify the model's behavior in the presence of backdoor triggers.
