A Novel fuzzy frame selection based watermarking scheme for MPEG-4 videos using Bi-directional extreme learning machine

With the advancement of Internet Technology, a lot of multimedia content is shared over the Internet resulting in several concerns over multimedia content like copyright protection, content authentication, and ownership. In this paper, we have developed a novel watermarking scheme for Advanced Video Coding (MPEG-4 AVC) videos using Bi-directional Extreme Learning Machine (B-ELM). Relevant frames to be watermarked are identified by implementing a newly developed fuzzy algorithm based on threshold operation. A binary watermark, encrypted by transposition cipher, is used to ensure enhanced security. The proposed watermarking scheme has been tested on five benchmark videos (Akiyo, Hall_Monitor, Mother_Daughter, Pamphlet, and Silent). High average Peak Signal to Noise Ratio (PSNR) values of the signed video sequences indicate good visual quality post-embedding. Common video interferences such as scaling, noising, cropping, filtering, frame dropping, frame averaging, and frame swapping are used to evaluate the robustness of the watermark embedding scheme. The high values of Normalized Correlation (NC(W, W')) and Bit-Error-Rate (BER(W, W')) between the original watermark (W) and the recovered watermark (W') establish the good robustness of the proposed scheme. While the established algorithms such as Fuzzy Inference System (FIS), Back Propagation Network (BPN), Meta-heuristic techniques take time of the order of several seconds to process a frame, the proposed algorithm consumes only a few milliseconds. Thus, it is well suited for the real-time watermarking of compressed videos. (C) 2018 Elsevier B.V. All rights reserved.