Image Tampering Detection Program Based on CFA (Color Filter Array) Interpolation Detection

This document presents an image tampering detection program based on CFA (Color Filter Array) interpolation detection. The program employs advanced algorithms to identify tampering artifacts in digital images. By analyzing CFA interpolation patterns, the system can accurately detect manipulation traces through statistical analysis of color correlation inconsistencies. The implementation involves preprocessing stages including Bayer pattern recognition and demosaicing artifact analysis. Key algorithms examine interpolation consistency across color channels using convolutional neural networks or statistical pattern recognition methods. The program architecture handles various tampering scenarios such as copy-move forgery, splicing, and retouching by evaluating local interpolation anomalies. This robust detection framework provides reliable tampering identification through features like energy distribution analysis and neighborhood correlation metrics. The system assists users in identifying and preventing image manipulation, thereby preserving image integrity and authenticity. Applications span digital forensics, copyright protection, and image security domains. The program incorporates adaptive thresholding mechanisms and multi-scale analysis to handle diverse image formats and compression levels. Through this CFA-based interpolation detection approach, we enhance image security verification with improved false-positive reduction and tampering localization capabilities.