从单张人脸合成图还原原始人脸，突破传统方法局限。
Facial Demorphing from a Single Morph Using a Latent Conditional GAN
- 在隐空间中分解合成图，实现跨技术还原
- 真实人脸测试下性能显著优于现有方法
- 适合安全验证与伪造检测场景使用
人脸合成图通过融合两个（或多个）身份的人脸图像生成，使合成图在生物特征上与多个个体高度相似，从而可能被关联到多个身份。人脸合成攻击检测（MAD）可识别合成图，但无法还原原始图像。因此，反合成（Demorphing）——还原原始人脸图像——至关重要。现有方法存在合成图复现问题，输出图像与原合成图过于相似，或假设训练与测试所用合成技术一致。本文提出的方法在隐空间中对合成图进行分解，可还原由未知合成技术与不同人脸风格生成的合成图。我们在合成人脸生成的训练数据上训练模型，并在真实人脸、不同合成技术生成的测试数据上进行评估。结果表明，本方法显著优于现有方法，能生成高保真度的还原人脸图像。
原文摘要 · Abstract (English)
A morph is created by combining two (or more) face images from two (or more) identities to create a composite image that is highly similar to all constituent identities, allowing the forged morph to be biometrically associated with more than one individual. Morph Attack Detection (MAD) can be used to detect a morph, but does not reveal the constituent images. Demorphing - the process of deducing the constituent images - is thus vital to provide additional evidence about a morph. Existing demorphing methods suffer from the morph replication problem, where the outputs tend to look very similar to the morph itself, or assume that train and test morphs are generated using the same morph technique. The proposed method overcomes these issues. The method decomposes a morph in latent space allowing it to demorph images created from unseen morph techniques and face styles. We train our method on morphs created from synthetic faces and test on morphs created from real faces using different morph techniques. Our method outperforms existing methods by a considerable margin and produces high fidelity demorphed face images.
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