Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another. We tackle the challenging task of inverting the latent space of pre-trained face recognition models without full model access (i.e. black-box setting). A variety of methods have been proposed in literature for this task, but they have serious shortcomings such as a lack of realistic outputs and strong requirements for the data set and accessibility of the face recognition model. By analyzing the black-box inversion problem, we show that the conditional diffusion model loss naturally emerges and that we can effectively sample from the inverse distribution even without an identity-specific loss. Our method, named identity denoising diffusion probabilistic model (ID3PM), leverages the stochastic nature of the denoising diffusion process to produce high-quality, identity-preserving face images with various backgrounds, lighting, poses, and expressions. We demonstrate state-of-the-art performance in terms of identity preservation and diversity both qualitatively and quantitatively, and our method is the first black-box face recognition model inversion method that offers intuitive control over the generation process.

翻译：人脸识别模型将人脸图像嵌入到低维身份向量中，该向量包含身份特异性面部特征的抽象编码，从而能够区分不同个体。我们解决了在不完全模型访问（即黑盒设置）下，对预训练人脸识别模型潜在空间进行逆变换这一具有挑战性的任务。文献中针对该任务提出了多种方法，但它们存在严重缺陷，例如缺乏逼真的输出，以及对数据集和人脸识别模型可访问性的强要求。通过分析黑盒逆变换问题，我们证明条件扩散模型损失自然产生，并且即使没有身份特异性损失，我们也能有效地从逆分布中采样。我们的方法名为身份去噪扩散概率模型（ID3PM），它利用去噪扩散过程的随机性，生成具有各种背景、光照、姿态和表情的高质量、保持身份特征的人脸图像。我们定性和定量地展示了在身份保持和多样性方面的最先进性能，并且我们的方法是首个提供对生成过程直观控制的的黑盒人脸识别模型逆变换方法。