A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications

Understand

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's production.

• Although some traditional visible copyright techniques are available, they would introduce undesired traces and result in a poor user experience.
• In this paper, we propose a novel plug-and-play invisible copyright protection method based on defensive perturbation for DNN-based applications (i.e., style transfer).
• Rather than apply the perturbation to attack the DNNs model, we explore the potential utilization of perturbation in copyright protection.