Adversarial Identity Injection for Semantic Face Image Synthesis
Giuseppe Tarollo Tomaso Fontanini Claudio Ferrari Guido Borghi Andrea Prati
Nowadays, deep learning models have reached incredible performance in the
task of image generation. Plenty of literature works address the task of face
generation and editing, with human and automatic systems that struggle to
distinguish what's real from generated. Whereas most systems reached excellent
visual generation quality, they still face difficulties in preserving the
identity of the starting input subject. Among all the explored techniques,
Semantic Image Synthesis (SIS) methods, whose goal is to generate an image
conditioned on a semantic segmentation mask, are the most promising, even
though preserving the perceived identity of the input subject is not their main
concern. Therefore, in this paper, we investigate the problem of identity
preservation in face image generation and present an SIS architecture that
exploits a cross-attention mechanism to merge identity, style, and semantic
features to generate faces whose identities are as similar as possible to the
input ones. Experimental results reveal that the proposed method is not only
suitable for preserving the identity but is also effective in the face
recognition adversarial attack, i.e. hiding a second identity in the generated
faces.