Deep neural network-based image copyright protection scheme

Haoyu Lu; Daofu Gong; Fenlin Liu; Ping Wang; Yuhan Kang

doi:10.1117/1.JEI.28.2.023021

26 March 2019 Deep neural network-based image copyright protection scheme

Haoyu Lu, Daofu Gong, Fenlin Liu, Ping Wang, Yuhan Kang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023021 (March 2019). https://doi.org/10.1117/1.JEI.28.2.023021

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.

Citation Download Citation

Haoyu Lu, Daofu Gong, Fenlin Liu, Ping Wang, and Yuhan Kang "Deep neural network-based image copyright protection scheme," Journal of Electronic Imaging 28(2), 023021 (26 March 2019). https://doi.org/10.1117/1.JEI.28.2.023021

Received: 19 October 2018; Accepted: 11 February 2019; Published: 26 March 2019

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