# Exposing Fake Images with Forensic Similarity Graphs

arXiv: https://arxiv.org/abs/1912.02861 · HTML: https://mlsanity.com/paper/1912.02861/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

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

- [Real-Time Deepfake Detection in the Real-World](https://mlsanity.com/paper/2406.09398/) (2024)
- [AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics](https://mlsanity.com/paper/2304.06870/) (2023)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Open Set Synthetic Image Source Attribution](https://mlsanity.com/paper/2308.11557/) (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)
- [E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data](https://mlsanity.com/paper/2404.08814/) (2024)
- [Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering](https://mlsanity.com/paper/2102.01439/) (2021)
- [Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools](https://mlsanity.com/paper/2305.05784/) (2023)
- [Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2506.05890/) (2025)
- [SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization](https://mlsanity.com/paper/2203.07824/) (2022)
- [Totems: Physical Objects for Verifying Visual Integrity](https://mlsanity.com/paper/2209.13032/) (2022)
- [Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces](https://mlsanity.com/paper/2211.12314/) (2022)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Transferable Adversarial Attack on Image Tampering Localization](https://mlsanity.com/paper/2309.10243/) (2023)
- [Exploring Saliency Bias in Manipulation Detection](https://mlsanity.com/paper/2402.07338/) (2024)
- [Identification of Social-Media Platform of Videos through the Use of Shared Features](https://mlsanity.com/paper/2109.03598/) (2021)
- [MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence](https://mlsanity.com/paper/2503.20991/) (2025)

## Abstract (arXiv)

We propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel abstract, graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called ``community structure'' in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure.