Detection and Localization of Multiple Image Splicing Using MobileNet V1

TL;DR

This paper presents a method for detecting and localizing multiple image splicing forgeries using a MobileNet V1 backbone with Mask R-CNN, outperforming ResNet variants on a custom dataset.

Contribution

It introduces a novel approach combining Mask R-CNN with MobileNet V1 for effective multiple image splicing forgery detection and localization.

Findings

Proposed model outperforms ResNet variants in accuracy.

Effective localization of forged regions in spliced images.

Calculates forgery percentage scores for detected regions.

Abstract

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).