Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats
Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting the traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the  $8\times 8$  block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.