Forensics of Decompressed JPEG Color Images Based on Chroma Subsampling

Abstract

Identification of the type of chroma subsampling in a decompressed JPEG color image stored in a lossless format is important in forensic analysis. It is useful in many forensic scenarios like detecting localized forgery and estimating the quantization step sizes in the chroma planes for source camera identification. In this work, we propose a machine learning-based method capable of identifying the chroma subsampling used in the compression process. The method is based on detecting the change in adjacent pixel correlations due to upsampling process in JPEG decompression. These changes in the correlation are measured using the two-sample Kolmogorov-Smirnov (KS) test statistic in different directions. The experimental results show the efficacy of the proposed method in identifying the chroma subsampling scheme.