WIBE: Watermarks for generated Images - Benchmarking & Evaluation
Abstract
As invisible image watermarking gains importance for verifying AI-generated content, consistency and reproducibility remain major challenges due to the diverse methods, datasets, attacks, and metrics.We aim to provide a flexible, extensible, and user-friendly framework that enables systematic testing of watermarking methods under various conditions.We developed WIBE, a framework with command-line interfaces and YAML configuration support, enabling users to evaluate a wide range of image watermarking algorithms on various datasets, apply configurable attack scenarios, and compute standard performance metrics. WIBE includes a library of pre-implemented methods and supports integration of new watermarking techniques, attacks, metrics, and datasets through a plugin-based architecture.WIBE enables rapid prototyping, reproducible experiments, and insightful comparison of watermarking robustness. In our demo, we present its core features, plugin extensibility, and interactive infographics, making it a practical tool for researchers and practitioners working at the intersection of AI and media integrity.Project on GitHub: https://github.com/ispras/wibeYouTube video: https://youtu.be/lbWWB1crrwk
BibTeX
@inproceedings{Yakushev-al:ASE25,
author = {Aleksey Yakushev and
Aleksandr Akimenkov and
Khaled Abud and
Dmitry Obydenkov and
Irina Serzhenko and
Kirill Aistov and
Egor Kovalev and
Stanislav A. Fomin and
Anastasia Antsiferova and
Kirill Lukianov and
Yury Markin},
title = {{WIBE:} Watermarks for generated Images - Benchmarking \& Evaluation},
booktitle = {ASE},
pages = {4033--4036},
publisher = {{IEEE}},
year = {2025},
}