Realistic Face Reconstruction from Facial Embeddings via Diffusion Models
Image generated by Gemini AI
Researchers have developed a framework called face embedding mapping (FEM) to assess privacy risks in privacy-preserving face recognition (PPFR) systems. By utilizing a Kolmogorov-Arnold Network and a pre-trained Identity-Preserving diffusion model, they successfully reconstructed high-resolution faces from embeddings. This method demonstrated effectiveness against both traditional and PPFR systems, revealing vulnerabilities that could allow unauthorized access to real-world FR applications. FEM serves as a tool for evaluating privacy leaks in these technologies, with experiments conducted using publicly available datasets.
New Framework Reveals Privacy Risks in Face Recognition Systems
A recent study has introduced a framework for reconstructing high-resolution face images from facial embeddings, raising significant privacy concerns for face recognition systems. This research demonstrates the potential for high-fidelity reconstructions to bypass security measures.
The face embedding mapping (FEM) framework employs a Kolmogorov-Arnold Network (KAN) to facilitate embedding-to-face attacks, exploiting vulnerabilities in state-of-the-art technologies. Experimental evaluations confirm that the reconstructed faces can compromise real-world face recognition systems.
Key Findings from the Study
- Reconstructed faces can access various existing face recognition systems, highlighting risks associated with current embedding technologies.
- The FEM framework demonstrates robustness in reconstructing faces from both partial and protected embeddings, suggesting a broader range of vulnerabilities.
The implications of these findings underscore the urgent need for enhanced security measures within face recognition technology to safeguard against potential privacy breaches.
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📰 Original Source: https://arxiv.org/abs/2602.13168v1
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