DETECTING DIGITAL IMAGE COPY MOVE FORGERY WITH IMAGE FORENSICS

Over the last several years, the growth of photo editing software has resulted in the emergence of a subject of active research in the field of digital image fraud detection. Copy Move Forgery Detection (CMFD), also known as passive forgery detection, is the subject of this study. It is used to photographs that have been manipulated via the use of the copy move technique. Oriented Features from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features (Oriented FAST and rotated BRIEF) are proposed as the feature extraction method for a CMFD technique that uses 2 Nearest Neighbor (2NN) with Hierarchical Agglomerative Clustering (HAC) as the feature matching method. This technique is proposed as a CMFD technique. The proposed CMFD approach was evaluated using images that were exposed to a variety of geometrical assaults at various points in time. Using the proposed method for assessments, which makes use of photographs from the MICC-F600 and MICC-F2000 databases, it is possible to attain an overall accuracy rate of 84.33% and 82.79% respectively. The True Positive Rate for forgery detection was more than 91% when applied to photographs that had been altered in a variety of ways, including using different degrees of rotation, magnification, and object translation.