Deep neural network-based image copyright protection scheme

Abstract

A deep neural network-based image copyright protection scheme is presented. Instead of modifying the images like traditional watermarking methods, the proposed scheme trains a neural network to extract the robust features from image blocks and then classify them to represent the copyright message. First, the original image and its multiple attacked images are divided into nonoverlapping blocks, a part of which will be selected as candidate blocks. Then, the copyright message bits, together with candidate blocks, constitute the training dataset for the network, enabling the trained network to serve as a copyright message extractor. With the proposed scheme, no quality loss will be caused, and moreover, superior robustness can be achieved due to the adaptive robust feature extraction. Our study also offers further insight into the rationalities and considerations in design. Extensive experiments on a wide range of images show that the proposed scheme possesses strong robustness to many attacks, including additive noise corruption, JPEG compression, filtering, and resizing.