# Forensic Similarity for Digital Images

arXiv: https://arxiv.org/abs/1902.04684 · HTML: https://mlsanity.com/paper/1902.04684/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 27 of 27)

- [Generalizable Synthetic Image Detection via Language-guided Contrastive Learning](https://mlsanity.com/paper/2305.13800/) (2023)
- [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)
- [Open Set Synthetic Image Source Attribution](https://mlsanity.com/paper/2308.11557/) (2023)
- [Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Images](https://mlsanity.com/paper/2503.21003/) (2025)
- [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)
- [Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization](https://mlsanity.com/paper/2408.02306/) (2024)
- [Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)](https://mlsanity.com/paper/2508.11716/) (2025)
- [Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2506.05890/) (2025)
- [Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion](https://mlsanity.com/paper/2208.13739/) (2022)
- [Progressive Feedback-Enhanced Transformer for Image Forgery Localization](https://mlsanity.com/paper/2311.08910/) (2023)
- [The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs](https://mlsanity.com/paper/2101.11081/) (2021)
- [SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization](https://mlsanity.com/paper/2203.07824/) (2022)
- [Noise and Edge Based Dual Branch Image Manipulation Detection](https://mlsanity.com/paper/2207.00724/) (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)
- [DF-Net: The Digital Forensics Network for Image Forgery Detection](https://mlsanity.com/paper/2503.22398/) (2025)
- [Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features](https://mlsanity.com/paper/2103.13674/) (2021)
- [A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network](https://mlsanity.com/paper/2101.09568/) (2021)
- [Identification of Social-Media Platform of Videos through the Use of Shared Features](https://mlsanity.com/paper/2109.03598/) (2021)
- [Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images](https://mlsanity.com/paper/2206.10737/) (2022)
- [H4VDM: H.264 Video Device Matching](https://mlsanity.com/paper/2210.11549/) (2022)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)
- [PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer](https://mlsanity.com/paper/2501.16227/) (2025)
- [MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence](https://mlsanity.com/paper/2503.20991/) (2025)

## Abstract (arXiv)

In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make a forensic similarity decision on it in the future. To do this, we propose a two part deep-learning system composed of a CNN-based feature extractor and a three-layer neural network, called the similarity network. This system maps pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated system accuracy of determining whether two image patches were 1) captured by the same or different camera model, 2) manipulated by the same or different editing operation, and 3) manipulated by the same or different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces, and importantly show efficacy on "unknown" forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.