# Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)

arXiv: https://arxiv.org/abs/1709.02016 · HTML: https://mlsanity.com/paper/1709.02016/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2017.

## Built on by (most cited first, 30 of 64)

- [Exploring Temporal Coherence for More General Video Face Forgery Detection](https://mlsanity.com/paper/2108.06693/) (2021)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models](https://mlsanity.com/paper/2410.02761/) (2024)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [Local Relation Learning for Face Forgery Detection](https://mlsanity.com/paper/2105.02577/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning](https://mlsanity.com/paper/2105.13617/) (2021)
- [CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation](https://mlsanity.com/paper/2107.02408/) (2021)
- [OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking](https://mlsanity.com/paper/2412.01615/) (2024)
- [One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework](https://mlsanity.com/paper/2105.00187/) (2021)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization](https://mlsanity.com/paper/2107.02434/) (2021)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning](https://mlsanity.com/paper/2105.06117/) (2021)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata](https://mlsanity.com/paper/2301.04647/) (2023)
- [Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization](https://mlsanity.com/paper/2406.13565/) (2024)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [Deep Video Inpainting Detection](https://mlsanity.com/paper/2101.11080/) (2021)
- [ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection](https://mlsanity.com/paper/2411.19466/) (2024)
- [DRAW: Defending Camera-shooted RAW against Image Manipulation](https://mlsanity.com/paper/2307.16418/) (2023)
- [Metric Learning for Anti-Compression Facial Forgery Detection](https://mlsanity.com/paper/2103.08397/) (2021)
- [Towards Effective Image Manipulation Detection with Proposal Contrastive Learning](https://mlsanity.com/paper/2210.08529/) (2022)
- [Inharmonious Region Localization](https://mlsanity.com/paper/2104.09453/) (2021)
- [ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization](https://mlsanity.com/paper/2211.03930/) (2022)

## Abstract (arXiv)

In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.