Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm

The widespread use of digital image alteration emphasizes how urgently reliable detection methods are needed to protect the originality and integrity of visual content. In this paper, we present an original technique for copy-move image forgery detection that combines Scale-Invariant Feature Transform (SIFT) with Convolutional Neural Networks . Our approach uses SIFT for reliable key-point descriptor extraction and Error Level Analysis (ELA) preprocessing to improve potentially changed regions. In parallel, a CNN model is trained using characteristics extracted from ELA representations to distinguish between modified and unmanipulated images. Although our hybrid methodology shows promising results in identifying copy-move forgeries, it is important to recognize the limits of current methods and systems.These drawbacks include the inability to grasp the results, scalability problems, dependency on handcrafted characteristics, computational complexity, limited generalization, partial copy-move vulnerabilities, and lack of interpretability. Our suggested method's incorporation of SIFT is essential for identifying forgeries, especially in situations where copy-move manipulation is involved. By offering robust and unique descriptors that are independent of scale, rotation, and translation, SIFT features provide precise recognition of replicated areas in a picture. This method improves the model's capacity to identify minute changes and visualise the location of forgery by utilizing SIFT in conjunction with CNN. This helps to maintain the visual authenticity and reliability of digital content.