# SAFIRE: Segment Any Forged Image Region

arXiv: https://arxiv.org/abs/2412.08197 · HTML: https://mlsanity.com/paper/2412.08197/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Guiding Instruction-based Image Editing via Multimodal Large Language Models](https://mlsanity.com/paper/2309.17102/) (2023)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection](https://mlsanity.com/paper/2304.04709/) (2023)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)
- [Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering](https://mlsanity.com/paper/2308.09307/) (2023)

## Built on by (most cited first, 18 of 18)

- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack](https://mlsanity.com/paper/2508.07402/) (2025)
- [AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era](https://mlsanity.com/paper/2504.11015/) (2025)
- [Towards Reliable Identification of Diffusion-based Image Manipulations](https://mlsanity.com/paper/2506.05466/) (2025)
- [Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios](https://mlsanity.com/paper/2509.20006/) (2025)
- [GS-Checker: Tampering Localization for 3D Gaussian Splatting](https://mlsanity.com/paper/2511.20354/) (2025)
- [ForgeryVCR: Visual-Centric Reasoning via Efficient Forensic Tools in MLLMs for Image Forgery Detection and Localization](https://mlsanity.com/paper/2602.14098/) (2026)
- [CLUE: Leveraging Low-Rank Adaptation to Capture Latent Uncovered Evidence for Image Forgery Localization](https://mlsanity.com/paper/2508.07413/) (2025)
- [Creating Blank Canvas Against AI-enabled Image Forgery](https://mlsanity.com/paper/2511.22237/) (2025)
- [Semantic Manipulation Localization](https://mlsanity.com/paper/2604.10132/) (2026)
- [CBDiff:Conditional Bernoulli Diffusion Models for Image Forgery Localization](https://mlsanity.com/paper/2510.19597/) (2025)
- [DinoLizer: Separating VAE and Diffusion Artifacts in Generative Inpainting Localization](https://mlsanity.com/paper/2511.20722/) (2025)
- [Off-the-shelf Vision Models Benefit Image Manipulation Localization](https://mlsanity.com/paper/2604.09096/) (2026)
- [Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization](https://mlsanity.com/paper/2606.02178/) (2026)
- [SARIF: Segment Anything for Robust Image Forensics](https://mlsanity.com/paper/2606.21108/) (2026)
- [GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization](https://mlsanity.com/paper/2608.20929/) (2026)
- [SegWave: Wavelet-Driven Segmentation of Tampered Regions](https://mlsanity.com/paper/2608.30714/) (2026)
- [Forensic-Aware Continual Adaptation for Image Forgery Localization](https://mlsanity.com/paper/2609.38251/) (2026)

## Abstract (arXiv)

Most techniques approach the problem of image forgery localization as a binary segmentation task, training neural networks to label original areas as 0 and forged areas as 1. In contrast, we tackle this issue from a more fundamental perspective by partitioning images according to their originating sources. To this end, we propose Segment Any Forged Image Region (SAFIRE), which solves forgery localization using point prompting. Each point on an image is used to segment the source region containing itself. This allows us to partition images into multiple source regions, a capability achieved for the first time. Additionally, rather than memorizing certain forgery traces, SAFIRE naturally focuses on uniform characteristics within each source region. This approach leads to more stable and effective learning, achieving superior performance in both the new task and the traditional binary forgery localization.