  Recently, there has been an increasing concern about the privacy issue raised
by identifiable information in machine learning. However, previous portrait
matting methods were all based on identifiable images. To fill the gap, we
present P3M-10k, which is the first large-scale anonymized benchmark for
Privacy-Preserving Portrait Matting (P3M). P3M-10k consists of 10,421 high
resolution face-blurred portrait images along with high-quality alpha mattes,
which enables us to systematically evaluate both trimap-free and trimap-based
matting methods and obtain some useful findings about model generalization
ability under the privacy preserving training (PPT) setting. We also present a
unified matting model dubbed P3M-Net that is compatible with both CNN and
transformer backbones. To further mitigate the cross-domain performance gap
issue under the PPT setting, we devise a simple yet effective Copy and Paste
strategy (P3M-CP), which borrows facial information from public celebrity
images and directs the network to reacquire the face context at both data and
feature level. Extensive experiments on P3M-10k and public benchmarks
demonstrate the superiority of P3M-Net over state-of-the-art methods and the
effectiveness of P3M-CP in improving the cross-domain generalization ability,
implying a great significance of P3M for future research and real-world
applications.
