The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images. This technological development has undoubtedly helped face recognition, such as training data augmentation for higher recognition accuracy and data privacy. However, it has also introduced novel challenges concerning the responsible use and proper attribution of computer generated images. We investigate the impact of digital watermarking, a technique for embedding ownership signatures into images, on the effectiveness of face recognition models. We propose a comprehensive pipeline that integrates face image generation, watermarking, and face recognition to systematically examine this question. The proposed watermarking scheme, based on an encoder-decoder architecture, successfully embeds and recovers signatures from both real and synthetic face images while preserving their visual fidelity. Through extensive experiments, we unveil that while watermarking enables robust image attribution, it results in a slight decline in face recognition accuracy, particularly evident for face images with challenging poses and expressions. Additionally, we find that directly training face recognition models on watermarked images offers only a limited alleviation of this performance decline. Our findings underscore the intricate trade off between watermarking and face recognition accuracy. This work represents a pivotal step towards the responsible utilization of generative models in face recognition and serves to initiate discussions regarding the broader implications of watermarking in biometrics.

翻译：摘要：生成模型的最新进展彻底革新了高度逼真图像（包括人脸图像）的合成技术。这一技术发展无疑助力了人脸识别，例如通过训练数据增强来提高识别精度和数据隐私保护。然而，它也带来了关于计算机生成图像的负责任使用与正确归属的新挑战。本文研究了数字水印技术（一种向图像嵌入所有权签名的方法）对人脸识别模型有效性的影响。我们提出了一套综合流程，整合了人脸图像生成、水印嵌入和人脸识别，以系统性地探讨这一问题。所提出的基于编码器-解码器架构的水印方案成功地在真实与合成人脸图像中嵌入并恢复签名，同时保持了图像的视觉保真度。通过大量实验，我们发现：虽然水印技术实现了稳健的图像归属，但会导致人脸识别精度轻微下降，尤其在具有挑战性姿态和表情的人脸图像上表现明显。此外，我们发现直接在带水印的图像上训练人脸识别模型仅能有限缓解这种性能下降。我们的研究结果凸显了水印技术与识别精度之间的复杂权衡。这项工作朝着在人脸识别中负责任地使用生成模型迈出了关键一步，并推动了关于水印技术在生物特征识别领域更广泛影响的讨论。