Deep Neural Feature Extraction for High-Precision Copy – Move Forgery Detection

Copy–move forgery is one of the generalized image manipulation techniques in digital forensics. In this work, deep neural feature extraction is employed to achieve high‐precision detection of tampered area, even under complex transformations such as blurring, noise addition, scaling, and geometric distortions. The proposed way integrates deep learning feature extraction with optimized machine learning classification to enhance accuracy in identifying manipulated image regions. The system processes the input image, extracts deep feature representations, reduces noise, and performs region-based matching to locate duplicated areas. Experiments conducted on multiple forged datasets demonstrate significant improvements in accuracy, robustness, and detection speed. The proposed deep neural feature extraction method effectively identifies copy–move forgeries with high precision, offering a scalable solution for digital image forensics.