PhotoHolmes: a Python library for forgery detection in digital images

TL;DR

PhotoHolmes is an open-source Python library that simplifies the application, benchmarking, and comparison of digital image forgery detection methods, promoting accessibility and reproducibility in the field.

Contribution

It introduces a modular, extensible library with benchmarking tools, dataset integration, and a CLI to facilitate forgery detection research and practice.

Findings

Includes implementations of popular and state-of-the-art forgery detection methods.

Provides tools for dataset management and evaluation metrics.

Enables easy comparison and benchmarking of forgery detection techniques.

Abstract

In this paper, we introduce PhotoHolmes, an open-source Python library designed to easily run and benchmark forgery detection methods on digital images. The library includes implementations of popular and state-of-the-art methods, dataset integration tools, and evaluation metrics. Utilizing the Benchmark tool in PhotoHolmes, users can effortlessly compare various methods. This facilitates an accurate and reproducible comparison between their own methods and those in the existing literature. Furthermore, PhotoHolmes includes a command-line interface (CLI) to easily run the methods implemented in the library on any suspicious image. As such, image forgery methods become more accessible to the community. The library has been built with extensibility and modularity in mind, which makes adding new methods, datasets and metrics to the library a straightforward process. The source code is available at https://github.com/photoholmes/photoholmes.