# Image Provenance Analysis at Scale

arXiv: https://arxiv.org/abs/1801.06510 · HTML: https://mlsanity.com/paper/1801.06510/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 16 of 16)

- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition](https://mlsanity.com/paper/2208.03138/) (2022)
- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [Motif Mining: Finding and Summarizing Remixed Image Content](https://mlsanity.com/paper/2203.08327/) (2022)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)
- [Benchmarking Scientific Image Forgery Detectors](https://mlsanity.com/paper/2105.12872/) (2021)
- [Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media](https://mlsanity.com/paper/2203.07167/) (2022)
- [Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale](https://mlsanity.com/paper/2206.00282/) (2022)
- [Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics](https://mlsanity.com/paper/2509.05753/) (2025)
- [GaussTrace: Provenance Analysis of 3D Gaussian Splatting Models with Evidence-based LLM Reasoning](https://mlsanity.com/paper/2606.10612/) (2026)
- [Detecting Near-Duplicate Face Images](https://mlsanity.com/paper/2408.07689/) (2024)
- [Embedding Provenance in Computer Vision Datasets with JSON-LD](https://mlsanity.com/paper/2603.27348/) (2026)
- [On the Origin of Synthetic Information by Means of Steganographic Inheritance](https://mlsanity.com/paper/2605.27551/) (2026)
- [Multi-Tool Image Editing Attribution in Facial Forgery](https://mlsanity.com/paper/2609.02751/) (2026)

## Abstract (arXiv)

Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation ( e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.