MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection
This addresses the problem of detecting image forgeries for applications in digital forensics, though it appears incremental as it builds on existing deep learning models.
The paper tackles image splicing detection by proposing a twin CNN network with feature-transfer learning, which improves detection accuracy for both coarse and fine forgeries across benchmark datasets.
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.