Method for detecting the results of digital image cloning in post-processing

The relevance of this study is driven by the increasing number of digital image falsification cases and the widespread use of fragment cloning as a common manipulation technique, which poses serious challenges to information security. The aim of this research was to develop and experimentally validate a robust method for detecting digital image cloning under post-processing conditions based on the analysis of the minimum mean block difference matrix, enabling reliable identification and spatial localisation of cloned regions and their original prototypes. The proposed method was based on iterative interval bisection combined with binary cross-analysis of block differences and was implemented in the Matlab environment. The experimental framework included modelling additive Gaussian, multiplicative and impulse noise, JPEG compression, spatial filtering, and combined distortions. Visual fidelity was quantitatively evaluated using the peak signal-to-noise ratio with a threshold value of signal-to-noise ratio PSNR > 37 dB. Computational experiments were conducted on central regions of digital images with a resolution of 512 × 512 pixels. Cloned regions occupied less than 0.85% of the image area, typically ranging from 0.098% to 0.39%, while smaller cloned areas below 0.098% were also considered. The analysis was performed on a randomly selected single colour channel using blockbased processing. In the absence of post-processing distortions, the global minimum of the minimum mean block difference function reached zero, resulting in maximum detection performance with a true positive rate of 100%. False positive rate evaluation on original images yielded values of 11%, 7.5%, and 3% for block sizes of 16, 24, and 32, respectively. Although the proposed approach demonstrates high detection sensitivity, the results indicate limitations in suppressing false positives compared to some state-of-the-art methods, highlighting the need for further optimisation