# Detection and Localization of Image Forgeries using Resampling Features and Deep Learning

arXiv: https://arxiv.org/abs/1707.00433 · HTML: https://mlsanity.com/paper/1707.00433/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2017.

## Built on by (most cited first, 13 of 13)

- [Detection and Localization of Facial Expression Manipulations](https://mlsanity.com/paper/2103.08134/) (2021)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection](https://mlsanity.com/paper/2411.19466/) (2024)
- [VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces](https://mlsanity.com/paper/2211.15775/) (2022)
- [Towards Effective Image Manipulation Detection with Proposal Contrastive Learning](https://mlsanity.com/paper/2210.08529/) (2022)
- [Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering](https://mlsanity.com/paper/2102.01439/) (2021)
- [TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization](https://mlsanity.com/paper/2108.04508/) (2021)
- [Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices](https://mlsanity.com/paper/2104.05693/) (2021)
- [Detection and Localization of Multiple Image Splicing Using MobileNet V1](https://mlsanity.com/paper/2108.09674/) (2021)
- [Seam Carving Detection and Localization using Two-Stage Deep Neural Networks](https://mlsanity.com/paper/2109.01764/) (2021)
- [SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery](https://mlsanity.com/paper/2108.12534/) (2021)
- [DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code](https://mlsanity.com/paper/2201.07583/) (2022)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)

## Abstract (arXiv)

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.