用于语义人脸图像合成的对抗性身份注入
Adversarial Identity Injection for Semantic Face Image Synthesis
AI总结：
针对语义人脸图像合成中身份保留不足的问题，提出融合身份、风格与语义特征的交叉注意力SIS架构，兼具身份保留与人脸识别对抗攻击能力。
摘要：
如今，深度学习模型在图像生成任务上已取得惊人的性能。大量研究工作聚焦人脸生成与编辑任务，其生成的图像让人类和自动系统都难以区分真伪。尽管大多数系统已实现出色的视觉生成质量，但它们在保留初始输入主体的身份方面仍存在困难。在所有已探索的技术中，目标是基于语义分割掩码生成图像的语义图像合成（Semantic Image Synthesis, SIS）方法最具前景，不过保留输入主体的感知身份并非其主要关注点。因此，本文研究了人脸图像生成中的身份保留问题，提出了一种SIS架构，该架构利用交叉注意力机制融合身份、风格和语义特征，生成身份与输入人脸尽可能相似的人脸。实验结果表明，所提方法不仅适用于身份保留，在人脸识别对抗攻击（即在生成的人脸中隐藏第二个身份）方面也十分有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
