Abstract
Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image-capturing technologies. The simplicity of changing image content thanks to advanced image-editing technologies presents fresh difficulties for forensic analysis. A structured hybrid framework is presented for finding important objects in images. It does this by using fast Fourier transformation (FFT) for frequency domain filtering, scale-invariant feature transformation (SIFT), and oriented FAST and rotated BRIEF (ORB) to pull out key points. The MobilenetV2 and VGG16 models extract features from key point areas to detect copy-move forgery. After that, an attention mechanism combines and normalizes these aspects. Key point matching uses the Euclidean distance; DBSCAN clustering groups pertinent key points for object localization. The suggested approach shows better performance than current methods and detects image copy-move forgery rather successfully. The framework's robustness is verified against image blurring, contrast alteration, color reduction, image compression, and brightness change among other post-processing techniques. Since photographs are altered, traditional approaches can struggle with a lot of variety; however, the proposed method combines advanced deep learning models and clustering techniques to make detection more accurate. Extensive testing on five benchmark copy-move forgeries datasets reveals that the suggested strategy may beat present techniques. This work offers a sophisticated automated approach to guarantee digital image integrity and identify image manipulation.