MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection

TL;DR

This paper introduces MissMarple, a socio-inspired twin CNN model with feature-transfer learning that enhances image splicing detection, especially for visually imperceptible forgeries, showing improved accuracy on multiple benchmark datasets.

Contribution

The paper presents a novel twin CNN architecture with feature-transfer learning specifically designed for improved image splicing detection.

Findings

Improved detection accuracy over existing models.

Effective on multiple benchmark datasets.

Capable of detecting visually imperceptible forgeries.

Abstract

In this paper we propose a novel socio-inspired convolutional neural network (CNN) deep learning model for image splicing detection. Based on the premise that learning from the detection of coarsely spliced image regions can improve the detection of visually imperceptible finely spliced image forgeries, the proposed model referred to as, MissMarple, is a twin CNN network involving feature-transfer learning. Results obtained from training and testing the proposed model using the benchmark datasets like Columbia splicing, WildWeb, DSO1 and a proposed dataset titled AbhAS consisting of realistic splicing forgeries revealed improvement in detection accuracy over the existing deep learning models.