In-Depth DCT Coefficient Distribution Analysis for First Quantization Estimation
This work addresses a specific challenge in digital forensics for investigators, but it appears incremental as it builds on existing methods with a mixed statistical and machine learning approach.
The paper tackles the problem of estimating first quantization factors in JPEG double compressed images to enable source camera model identification, achieving results that demonstrate effectiveness without prior assumptions about quantization matrices.
The exploitation of traces in JPEG double compressed images is of utter importance for investigations. Properly exploiting such insights, First Quantization Estimation (FQE) could be performed in order to obtain source camera model identification (CMI) and therefore reconstruct the history of a digital image. In this paper, a method able to estimate the first quantization factors for JPEG double compressed images is presented, employing a mixed statistical and Machine Learning approach. The presented solution is demonstrated to work without any a-priori assumptions about the quantization matrices. Experimental results and comparisons with the state-of-the-art show the goodness of the proposed technique.