In modern society, digital images have become a prominent source of
information and medium of communication. They can, however, be simply altered
using freely available image editing software. Two or more images are combined
to generate a new image that can transmit information across social media
platforms to influence the people in the society. This information may have
both positive and negative consequences. Hence there is a need to develop a
technique that will detect and locates a multiple image splicing forgery in an
image. This research work proposes multiple image splicing forgery detection
using Mask R-CNN, with a backbone as a MobileNet V1. It also calculates the
percentage score of a forged region of multiple spliced images. The comparative
analysis of the proposed work with the variants of ResNet is performed. The
proposed model is trained and tested using our MISD (Multiple Image Splicing
Dataset), and it is observed that the proposed model outperforms the variants
of ResNet models (ResNet 51,101 and 151).

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