NN Watermarking for Face Segmentation Task

Semantic segmentation tasks have become a core component in a wide range of computer vision applications, ranging from face segmentation for access security, to customized goodies distributing in e-gamming or unmanned car navigation. As this methodological framework keeps evolving, the need to track the intellectual property rights for semantic segmentation neural networks arises. Watermarking is an appealing solution in this respect, as it already proved its effectiveness in solving similar concerns related to multimedia content. The present paper reports on a study on the use of watermarking for NN-based face segmentation models and their inferences. From the methodological point of view, a watermarking solution is advanced and subsequently instantiated for covering the needs of two watermarking insertion strategies, namely post-training white-box and encapsulated black-box. Beyond its applicative utility, this also shows how synergies between image and neural network watermarking can be established. The experimental results consider the imperceptibility and robustness properties, evaluated for 4 face segmentation datasets (CelebA, LaPa, LFW, HELEN), and 3 NN models (DeepLab, FCN, and LRASPP), thus serving as a proof of concepts for NN watermarking for face segmentation.