Generalized Local Optimality for Video Steganalysis in Motion Vector Domain

Video steganography that conceals secret data into motion vectors (MVs) is a popular covert communication technique. The local optimality of MVs is an intrinsic property in video coding, and any modifications to the MVs will inevitably destroy this optimality, making it a sensitive indicator of steganography. Thus the local optimality is commonly used to design features in video steganalysis. However, the local optimality in existing works is often estimated inaccurately or by using an unreasonable assumption, limiting its capability in steganalysis. In this article, we propose to estimate the local optimality in a more reasonable and comprehensive fashion, and generalize the local optimality in two aspects. First, we generalize the local optimality from a static estimation to a dynamic one by considering the variability of predicted motion vectors (PMVs). Second, we generalize the local optimality from MV domain to PMV domain by leveraging the statistical anomaly of PMVs. Based on the two generalizations that ensure a more accurate estimation of local optimality from more views, we construct new types of steganalytic features and also propose feature symmetrization rules to reduce feature dimension. Extensive experiments demonstrate the superiority of the proposed features, which achieve state-of-the-art accuracy and robustness under various conditions.