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<p>In this paper, we introduce <i>PhotoHolmes</i>, an open-source <i>Python</i> 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 <i>Benchmark</i> tool in <i>PhotoHolmes</i>, users can effortlessly compare various methods. This facilitates an accurate and reproducible comparison between their own methods and those in the existing literature. Furthermore, <i>PhotoHolmes</i> includes a command-line interface&#xa0;(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 <a href="https://github.com/photoholmes/photoholmes">https://github.com/photoholmes/photoholmes</a>.</p>

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PhotoHolmes: a Python library for forgery detection in digital images

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Julián O’Flaherty,
  • Rodrigo Paganini,
  • Juan Pablo Sotelo,
  • Julieta Umpiérrez,
  • Marina Gardella,
  • Matías Tailanián,
  • Pablo Musé

摘要

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.