Motion vector domain video steganography method based on novel embedding cost

Abstract

The invention relates to a motion vector domain video steganography method based on a novel embedding cost. The novel embedding cost construction method gives fully consideration to affects on motion characteristics, motion vector local optimality and motion vector statistic distribution of a video content because of motion vector alteration and uses weight parameters to regulate distribution proportion of three costs in a novel cost. The motion vector domain video steganography method adopts an adaptive selection strategy when selecting a replaceable motion vector, can effectively maintains the local optimality of the motion vector, and, particularly under a condition of high bit rate, can obtain higher safety compared with the current steganography method. The motion vector domain video steganography method can effectively resist multiple steganalysis like the steganalysis method based on the local optimality, the steganalysis method based on double compression, etc.

Description

technical field [0001] The present invention relates to video steganography (Steganograhpy), in particular to a novel embedding cost construction method based on motion vectors, and an adaptive video steganography method using the novel embedding cost. The method belongs to the information hiding subclass in the field of information security technology. field. Background technique [0002] As an important branch of information hiding, steganography can achieve the purpose of covert communication by embedding secret messages into multimedia files (such as digital images, audio, video, text, etc.). The multimedia files before and after steganography are visually and statistically indistinguishable, so they will not arouse the suspicion of the attacker. With the rapid development of advanced video compression technology and computer network technology, digital video has become one of the most influential files in multimedia applications. Due to the advantages of abundant data...

AI Technical Summary

Problems solved by technology

For example Cao 1 The algorithm and Yao's steganalysis method cannot resist steganalysis methods based on local optimality, while Zhang and Cao 2 Steganographic methods with a high probability of being successfully detected by calibration-based steganalysis methods

Furthermore, most cost functions are defined in terms of the effect of steganographic operations on a single video feature, and existing cost definitions are not general to rich and diverse videos due to the heavy dependence on the chosen compressed video

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