Adversarial Identity Injection for Semantic Face Image Synthesis
This addresses identity preservation in face generation for applications like security and editing, but it is incremental as it builds on existing SIS methods.
The paper tackles the problem of preserving identity in semantic face image synthesis by introducing an architecture that merges identity, style, and semantic features using cross-attention, resulting in generated faces with identities similar to input ones and effectiveness in adversarial attacks for face recognition.
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.