You are in:Home/Publications/Acharjee S, Chakraborty S, Samanta S, Azar AT, Dey N, Hassanien AE (2014) Highly secured multilayered motion vector watermarking. In: A.E. Hassanien, M.F. Tolba, A.T. Azar (eds.), Advanced Machine Learning Technologies and Applications, Communications in Computer and Information Science, Vol. 488, pp. 121-134, Springer-Verlag GmbH Berlin/Heidelberg. DOI: 10.1007/978-3-319-13461-1_13

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Acharjee S, Chakraborty S, Samanta S, Azar AT, Dey N, Hassanien AE (2014) Highly secured multilayered motion vector watermarking. In: A.E. Hassanien, M.F. Tolba, A.T. Azar (eds.), Advanced Machine Learning Technologies and Applications, Communications in Computer and Information Science, Vol. 488, pp. 121-134, Springer-Verlag GmbH Berlin/Heidelberg. DOI: 10.1007/978-3-319-13461-1_13
Authors: Not Available
Year: 2014
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

With the recent development in multimedia, video has become a powerful medium of information. To exploit the temporal redundancy during video compression, motion vector estimation is required. Now-a-days internet and digital media has become very popular, which made data authentication and data security a challenging task. Digital watermarking was introduced to provide data authentication. Though, it was not enough to prevent the unauthorized access of data by third parties. To prevent unauthorized access, data is encrypted using a secret key known only to the user. This process is known as cryptography. In this paper, an algorithm has been proposed to embed the watermark inside calculated motion vector. The position of watermark bit inside the motion vector will depend on a key provided by user. The correlation values between the four original and recovered experimental video frames are 0.97, 0.98, 0.98 and 0.91 respectively whereas structural similarity index metric (SSIM) between them are 0.97, 0.87, 0.90 and 0.68, respectively. The high correlation values and SSIM shows the effectiveness of the proposed method.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus