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Experimental Result Paper | Forensic Science | India | Volume 11 Issue 7, July 2022

Copy Move Forgery Detection Using an Effective CNN Model

Siva Prasad Patnayakuni

Abstract: Recently, computerized pictures have become utilized in several applications, where they have turned into the focal point of advanced picture handling analysts. Picture false addresses one exciting issue on which analysts focus on their examinations. We focus on the copy move picture fake point as a misleading fraud type. In duplicate move picture imitation, a piece of a picture is replicated and set in a similar picture to create the invention picture. In this paper, a specific convolutional neural network (CNN) design is proposed for the compelling recognition of duplicate move picture fabrication. The proposed Method is computationally lightweight with a reasonable number of convolutional and max-pooling layers. Numerous observational tests have been led to guarantee the effectiveness of the proposed model with regards to accuracy and time. These analyses were finished on benchmark datasets and have achieved 95.53% accuracy.

Keywords: convolutional neural network, CNN, CMFD, accuracy

Edition: Volume 11 Issue 7, July 2022,

Pages: 758 - 764