# Noise and Edge Based Dual Branch Image Manipulation Detection

arXiv: https://arxiv.org/abs/2207.00724 · HTML: https://mlsanity.com/paper/2207.00724/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Asymmetric Non-local Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/1908.07678/) (2019)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Learning a Discriminative Feature Network for Semantic Segmentation](https://mlsanity.com/paper/1804.09337/) (2018)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)](https://mlsanity.com/paper/1709.02016/) (2017)
- [Generate, Segment and Refine: Towards Generic Manipulation Segmentation](https://mlsanity.com/paper/1811.09729/) (2018)
- [Constrained R-CNN: A general image manipulation detection model](https://mlsanity.com/paper/1911.08217/) (2019)
- [Forensic Similarity for Digital Images](https://mlsanity.com/paper/1902.04684/) (2019)

## Built on by (most cited first, 3 of 3)

- [Image Generation and Learning Strategy for Deep Document Forgery Detection](https://mlsanity.com/paper/2311.03650/) (2023)
- [A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization](https://mlsanity.com/paper/2409.00896/) (2024)
- [Wavelet based inpainting detection](https://mlsanity.com/paper/2408.06429/) (2024)

## Abstract (arXiv)

Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation. Meanwhile, the dual-branch network, consisting of a high-resolution branch and a context branch, is used to capture the traces of artifacts as much as possible. In general, most manipulation leaves manipulation artifacts on the manipulation edge. A specially designed manipulation edge detection module is constructed based on the dual-branch network to identify these artifacts better. The correlation between pixels in an image is closely related to their distance. The farther the two pixels are, the weaker the correlation. We add a distance factor to the self-attention module to better describe the correlation between pixels. Experimental results on four publicly available image manipulation datasets demonstrate the effectiveness of our model.