In-Depth DCT Coefficient Distribution Analysis for First Quantization Estimation

TL;DR

This paper introduces a novel method combining statistical analysis and machine learning to estimate the first quantization factors in JPEG double compressed images, aiding source camera identification without prior assumptions.

Contribution

The paper presents a new technique for first quantization estimation that does not rely on prior knowledge of quantization matrices, improving forensic analysis of JPEG images.

Findings

Effective estimation of first quantization factors demonstrated

Outperforms state-of-the-art methods in experiments

Applicable without assumptions on quantization matrices

Abstract

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.