SlerpFace: Face Template Protection via Spherical Linear Interpolation

TL;DR

SlerpFace introduces a novel face template protection method using spherical linear interpolation to enhance privacy and resist diffusion model-based inversion attacks, maintaining recognition accuracy.

Contribution

The paper proposes SlerpFace, a new face template protection technique employing spherical interpolation and feature dimension dropout for improved security and irreversibility.

Findings

Provides effective protection against inversion attacks

Maintains high face recognition accuracy

Outperforms prior template protection methods

Abstract

Contemporary face recognition systems use feature templates extracted from face images to identify persons. To enhance privacy, face template protection techniques are widely employed to conceal sensitive identity and appearance information stored in the template. This paper identifies an emerging privacy attack form utilizing diffusion models that could nullify prior protection. The attack can synthesize high-quality, identity-preserving face images from templates, revealing persons' appearance. Based on studies of the diffusion model's generative capability, this paper proposes a defense by rotating templates to a noise-like distribution. This is achieved efficiently by spherically and linearly interpolating templates on their located hypersphere. This paper further proposes to group-wisely divide and drop out templates' feature dimensions, to enhance the irreversibility of rotated templates. The proposed techniques are concretized as a novel face template protection technique, SlerpFace. Extensive experiments show that SlerpFace provides satisfactory recognition accuracy and comprehensive protection against inversion and other attack forms, superior to prior arts.