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

arXiv: https://arxiv.org/abs/2112.08018 · HTML: https://mlsanity.com/paper/2112.08018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 98 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection](https://mlsanity.com/paper/1812.02510/) (2018)
- [Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)](https://mlsanity.com/paper/1709.02016/) (2017)
- [Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection](https://mlsanity.com/paper/1703.04615/) (2017)
- [Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks](https://mlsanity.com/paper/1706.07842/) (2017)
- [Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution](https://mlsanity.com/paper/1809.02791/) (2018)

## Abstract (arXiv)

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.