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Exposing video forgeries by detecting MPEG double compression

In this paper, an improved video tampering detection model based on MPEG double compression is proposed. Double compression will import disturbance into Discrete Cosine Transform (DCT) coefficients, reflecting in the violation of the parametric logarithmic law for first digit distribution of quantized Alternating Current (AC) coefficients. A 12-D feature can be extracted from each group of pictures (GOP) and machine learning framework is adopted to enhance the detection accuracy. Furthermore, a novel approach with a serial Support Vector Machine (SVM) architecture to estimate original bit rate scale in doubly compressed video is proposed. Experiments demonstrate higher accuracy and effectiveness.