Video steganography method based on motion vector local optimality preservation

Abstract

The present invention relates to a video steganography method based on motion vector local optimality preservation. Criteria for motion vector local optimality and a search algorithm for optimal candidate motion vectors are established. On this basis, by constructing a reasonable motion vector disturbance cost calculation model and selecting the most efficient steganographic codes, covert channels are established to complete steganographic insertion of secret information, and an extraction process of the secrete information is thus completed. Syndrome trellis code (STC) steganographic codes are preferably adopted. The method provided by the present invention is a motion vector field video steganography method which is based on motion vector local optimality preservation, has a high insertion efficiency and can effectively resist conventional steganalysis attacks, and is especially applicable to covert communication situations with high safety requirements.

Description

technical field [0001] The present invention relates to a video steganography (Video Steganography) method, in particular to a high-concealment video steganography method based on motion vector local optimality preservation and its application in covert communication. The method belongs to the technical field of information security The information hiding subfield in . Background technique [0002] Modern information hiding technology mainly includes Steganography, Steganalysis and Digital Watermarking. Steganography mainly studies how to embed secret information into digital multimedia files such as images, videos, and audios to achieve the purpose of covert communication; steganalysis mainly uses methods such as machine learning and pattern recognition to classify and judge the files under test. [0003] In general, the design of a steganographic algorithm needs to comprehensively consider and weigh the following elements: [0004] 1) Imperceptibility: The carrier before...

AI Technical Summary

Problems solved by technology

[0008] 5) Security: Even with prior knowledge about the steganography algorithm, the existing steganalysis methods cannot distinguish between ordinary carriers and files steganographiced by the algorithm with a high enough probability

However, since the patented method still modifies the motion vector in the traditional way of "adding or subtracting one to a certain component of the motion vector", that is (mv' x ,mv' y )∈{(mv x +1, mv y ),(mv x -1,mv y ),(mv x ,mv y +1),(mv x ,mv y -1)}(where (mv x ,mv y ) and (mv' x ,mv' y ) represent the original and steganographically modified motion vectors respectively), so it is possible to destroy the local optimality of the motion vector (Local Optimality), so it cannot resist the attack of AoSO, the most effective steganalysis feature in the current motion vector field (refer to Literature: K.Wang, H.Zhao, and H.Wang. Video Steganalysis Against MotionVector-Based Steganography by Adding or Subtracting One Motion VectorValue. IEEE Transactions on Information Forensics and Security, vol.9(5):741-751, 2014 .)

Images