SlerpFace: Face Template Protection via Spherical Linear Interpolation

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, referred to as inversion attacks. 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 to deteriorate the attack, by rotating templates to a noise-like distribution. This is achieved efficiently by spherically and linearly interpolating templates, or slerp, 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 division of groups and dropouts within each group are learned in a recognition-favored way. The proposed techniques are concretized as a novel face template protection technique, SlerpFace. Extensive experiments show that SlerpFace provides satisfactory recognition accuracy and comprehensive privacy protection against inversion and other attack forms, superior to prior arts.