A cooperative three-player game theory approach for designing an ideal video steganography framework

ABSTRACTABSTRACTThis paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes.KEYWORDS: Video steganographygame theoryblock matching motion estimation algorithmArnold transformperceptible invisibilitypayload capacityrobustnessiterative elimination of strictly dominant strategies Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSuganthi KumarSuganthi Kumar received her BTech degree in information technology and her master of engineering degree in computer science and engineering from Anna University in 2015 and 2017, respectively, and has secured university rank. She is currently pursuing her PhD at Vellore Institute of Technology. Her areas of interest include image and video steganography/watermarking, image processing, cryptosystems, and optimization techniques.Rajkumar SoundrapandiyanRajkumar Soundrapandiyan received his PhD in digital image processing from Vellore Institute of Technology, Vellore, India, in 2017. He is currently working as an associate professor at the School of Computer Science and Engineering of Vellore Institute of Technology. He is a life member of Computer Society of India and a senior member of IEEE. He has 10 years of teaching experience. His research areas of interest include digital image processing, medical image analysis, computer vision, image feature description, and biometrics.