  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.
