# Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1706.07842 · HTML: https://mlsanity.com/paper/1706.07842/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 8 of 8)

- [Synthetic Data for Object Classification in Industrial Applications](https://mlsanity.com/paper/2212.04790/) (2022)
- [Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks](https://mlsanity.com/paper/2111.13970/) (2021)
- [Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery](https://mlsanity.com/paper/2304.09874/) (2023)
- [Smart Data Representations: Impact on the Accuracy of Deep Neural Networks](https://mlsanity.com/paper/2111.09128/) (2021)
- [MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection](https://mlsanity.com/paper/2112.08018/) (2021)
- [Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2203.02270/) (2022)
- [Dual-Stream Global-Local Feature Collaborative Representation Network for Scene Classification of Mining Area](https://mlsanity.com/paper/2507.20216/) (2025)
- [DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification](https://mlsanity.com/paper/2607.16338/) (2026)

## Abstract (arXiv)

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.