一句话：提出 WIBE，一个可扩展、可复现的图像水印基准测试框架，支持插件化方法与攻击场景。
随着不可见图像水印在验证 AI 生成内容方面日益重要，由于方法、数据集、攻击和指标多样，评估的一致性与可复现性仍是主要挑战。本文提出 WIBE，一个灵活、可扩展且用户友好的框架，提供命令行接口与 YAML 配置，使用户能够在多种数据集上评估各类图像水印算法，施加可配置的攻击场景，并计算标准性能指标。WIBE 内置预实现方法库，并通过插件化架构支持集成新的水印技术、攻击、指标与数据集。该框架支持快速原型、可复现实验以及水印鲁棒性的深入比较。演示展示了其核心功能、插件扩展性与交互式信息图，使其成为面向 AI 与媒体完整性交叉领域研究者和实践者的实用工具。
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
DOI 原文 ·
@inproceedings{paperbot9287,
title = {WIBE: Watermarks for generated Images – Benchmarking & Evaluation},
author = {Aleksey Yakushev and Aleksandr Akimenkov and Khaled Abud and Dmitry Obydenkov and Irina Serzhenko and Kirill Aistov and Egor Kovalev and Stanislav Fomin and Anastasia Antsiferova and Kirill Lukianov and Yury Markin},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00368}
}