Sparse Black-Box Inversion Attack with Limited Information

Existing black-box model inversion attacks mainly focus on training and attacking surrogate models. However, due to the deployment process of face recognition models, training surrogate models becomes extremely difficult in practice. At the same time, query-based black-box inversion attacks still suffer from low image quality and high computational costs. To bridge these gaps, in this paper, we propose BMI-S, a sparse black-box inversion attack against face recognition models. BMI-S first introduces evolution strategies to perform efficient black-box gradient estimation and achieve query-based attacks. Meanwhile, BMI-S performs sparse attacks on the key styles that contribute most to the face recognition process. By only optimizing key style control vectors, BMI-S further narrows the dimensions of the search space and accelerates the inversion attacks.