mlsanityData to 7 Oct 2026

Adversarial Identity Injection for Semantic Face Image Synthesis

arXiv 2404.10408 ↗First posted 16 Apr 2024cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 16 Apr 2024.

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34 of 47 references.

References · 34

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22 more
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  1. 2025SISMA: Semantic Face Image Synthesis with Mambacited by 0