# Constrained R-CNN: A general image manipulation detection model

arXiv: https://arxiv.org/abs/1911.08217 · HTML: https://mlsanity.com/paper/1911.08217/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 29 of 29)

- [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)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer](https://mlsanity.com/paper/2307.14863/) (2023)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [Multi-spectral Class Center Network for Face Manipulation Detection and Localization](https://mlsanity.com/paper/2305.10794/) (2023)
- [Comprint: Image Forgery Detection and Localization using Compression Fingerprints](https://mlsanity.com/paper/2210.02227/) (2022)
- [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)
- [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://mlsanity.com/paper/2302.13004/) (2023)
- [A New Benchmark and Model for Challenging Image Manipulation Detection](https://mlsanity.com/paper/2311.14218/) (2023)
- [Inharmonious Region Localization](https://mlsanity.com/paper/2104.09453/) (2021)
- [MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization](https://mlsanity.com/paper/2312.01790/) (2023)
- [Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Framework](https://mlsanity.com/paper/2508.01338/) (2025)
- [Noise and Edge Based Dual Branch Image Manipulation Detection](https://mlsanity.com/paper/2207.00724/) (2022)
- [Inharmonious Region Localization by Magnifying Domain Discrepancy](https://mlsanity.com/paper/2209.15368/) (2022)
- [Learning Hierarchical Graph Representation for Image Manipulation Detection](https://mlsanity.com/paper/2201.05730/) (2022)
- [Inharmonious Region Localization with Auxiliary Style Feature](https://mlsanity.com/paper/2210.02029/) (2022)
- [MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization](https://mlsanity.com/paper/2211.03140/) (2022)
- [A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization](https://mlsanity.com/paper/2409.00896/) (2024)
- [Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense](https://mlsanity.com/paper/2409.17941/) (2024)
- [Inharmonious Region Localization via Recurrent Self-Reasoning](https://mlsanity.com/paper/2210.02036/) (2022)
- [Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features](https://mlsanity.com/paper/2310.06525/) (2023)
- [Trusted Video Inpainting Localization via Deep Attentive Noise Learning](https://mlsanity.com/paper/2406.13576/) (2024)
- [Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network](https://mlsanity.com/paper/2601.02566/) (2026)
- [SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization](https://mlsanity.com/paper/2601.06222/) (2026)

## Abstract (arXiv)

Recently, deep learning-based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these issues, we propose a coarse-to-fine architecture named Constrained R-CNN for complete and accurate image forensics. First, the learnable manipulation feature extractor learns a unified feature representation directly from data. Second, the attention region proposal network effectively discriminates manipulated regions for the next manipulation classification and coarse localization. Then, the skip structure fuses low-level and high-level information to refine the global manipulation features. Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region. Experimental results show that our model achieves state-of-the-art performance. Especially, the F1 score is increased by 28.4%, 73.2%, 13.3% on the NIST16, COVERAGE, and Columbia dataset.