Multi-channel Prediction Residue Modeling (MPRM) Using Second Order Residual Statistics for Enhanced CFA Artifact Based Forgery Detection

In recent years advanced image editing tools are easily available to tamper the images in visually undetectable form. This created an urgent need of reliable and robust technique to authenticate image integrity. Digital camera produces the full color image through interpolating remaining channels which creates periodic artifacts known as Color Filter Array Artifacts (CFAA). In forged image these artifact consistency is disturbed, which is often used to detect and localize the forgery in tampered images. Existing CFAA based splicing detection methods often rely on single channel, exhibit high computational complexity and show degraded performance under JPEG compression. Although some work have explored multi-channel CFA based approaches but their ability to effectively capture cross channel dependencies and maintain robustness under heavy JPEG compression remains limited. To address these gaps, we propose a splicing detection framework that performs second order statistical analysis on residuals extracted from all color channels. Unlike existing multichannel CFAA techniques, this work explicitly models inter-channel relationships through the Error Variance Ratio (EVR) and introduces a novel Inter Block Mean Square Error (IBMSE) metric. This formulation enhances the characterization of CFAA periodicity and improves discrimination between authentic and tampered regions. The proposed technique is evaluated on CUISDE, RTD and IMD datasets and compared with existing CFA based localization methods using ROC, precision-recall and AUC metric. Experimental results demonstrate that the proposed method improves localization performance and shows robustness against varying levels of JPEG compression.