  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.
