Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

Abstract

Despite all its irrefutable benefits, the development of steganography methods has sparked ever-increasing concerns over steganography abuse in recent decades. To prevent the inimical usage of steganography, steganalysis approaches have been introduced. Since motion vector manipulation leads to random and indirect changes in the statistics of videos, MV-based video steganography has been the center of attention in recent years. In this paper, we propose a 54-dimentional feature set exploiting spatio-temporal features of motion vectors to blindly detect MV-based stego videos. The idea behind the proposed features originates from two facts. First, there are strong dependencies among neighboring MVs due to utilizing rate-distortion optimization techniques and belonging to the same rigid object or static background. Accordingly, MV manipulation can leave important clues on the differences between each MV and the MVs belonging to the neighboring blocks. Second, a majority of MVs in original videos are locally optimal after decoding concerning the Lagrangian multiplier, notwithstanding the information loss during compression. Motion vector alteration during information embedding can affect these statistics that can be utilized for steganalysis. Experimental results have shown that our features’ performance far exceeds that of state-of-the-art steganalysis methods. This outstanding performance lies in the utilization of complementary spatio-temporal statistics affected by MV manipulation as well as feature dimensionality reduction applied to prevent overfitting. Moreover, unlike other existing MV-based steganalysis methods, our proposed features can be adjusted to various settings of the state-of-the-art video codec standards such as sub-pixel motion estimation and variable-block-size motion estimation.

A Review of Motion Vector-Based Video Steganography

“…In contrast, Wang et al [91] proposed a four-way scanning method applicable to variable block size and designed 392-dimensional features based on the correlation anomaly of MVs, which can effectively detect methods such as those in the literature [39,45]. In addition, Ghamsarian et al [92] designed a method by considering intraframe statistical features, interframe statistical features, and local optimality of MVs, which further improved the performance of steganalysis.…”

Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research

“…According to the experiment, the detection accuracy reaches approximately 93%. Ghamsarian et al [64] proposed a blind technique to detect many types of MV steganography. A novel feature called MVs' Spatio-Temporal features termed (MVST) is proposed where consists of 36 and 18 spatial and temporal feature sets, respectively.…”

A comprehensive review of video steganalysis

“…Also see how to apply the trained CNN-based model to detect object forgery for lower bitrate video sequence or lower resolution video sequence, which is named as transfer learning in deep learning research. This approach can even be associated with the suggestion of Ghamsarian et al in [79] to detect hidden messages in deformable objects using object detection and tracking techniques like in [114,126].…”

“…The last works, as per the time of publication of this paper, namely 2021, Liu et al[78] extracted a one-dimensional descriptor that computes the difference between the coding cost of a video before and after recoding. The same year, Ghamsarian et al in[79] extracted 54 features exploiting the dependencies among neighbouring blocks, and the modification of the statistical Lagrangian multiplier value.…”