Abstract
Watermarking is a technique used for digital copyright protection. It has been developed to protect digital media from being illegally reproduced and modified. In this paper, a full counter-propagation neural network (FCNN) is applied to copyright authentication, where the ownership information (watermark) is embedded and detected by a specific FCNN. As opposed to traditional methods, the watermark is stored in the synapses of the FCNN. Because an FCNN has storage and fault tolerance, most attacks do not degrade the quality of the detected watermark image. Moreover, the watermark embedding procedure and detection procedure are integrated into the proposed FCNN. It accomplishes watermark embedding and detection of one or many watermarks using a multi-cover image. The experimental results show that the proposed method is more robust than Deng’s, Hsieh’s, Hsu’s, and Zhang’s methods under different attacks, and that it is imperceptible and authentic.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
