Exploiting AC Histogram Statistics for Misalignment Estimation in Double JPEG Compressed Images

A critical task in forensics investigation is the recovery of the manipulation history of the image under analysis. To this aim, considering a typical life-cycle of a digital image, the estimation of the misalignment occurred between consecutive JPEG compressions can be considered an useful starting point to localize forgeries and retrieve information about the camera that took the picture through first quantization matrix estimation. In this work, starting from statistics computed from the AC histograms obtained applying a third JPEG compression, an effective and robust deep learning based approach devoted to estimate the aforementioned misalignment has been designed. Finally, to assess the performance of the proposed solution a series of tests has been conducted at varying of patch sizes, quantization matrices, employed datasets, and comparisons with state-of-the-art solutions.