The growing popularity and capacity of generative models have eroded the distinction between human and machine-generated content, motivating a growing body of work on detection across text, images, and audio. Most available detectors are either commercial software or, if open-source, come with incompatible codebases with bespoke preprocessing, evaluation protocols, and evaluation metrics, which make their adoption, fair comparison, and reproduction quite difficult. To address this critical gap, we introduce DetectZoo, a first-of-its-kind, extensible toolkit designed to provide a unified interface for AI-generated content detection across text, audio, and image modalities. DetectZoo standardizes the complete empirical pipeline, from data ingestion and preprocessing to model assessment, offering researchers a cohesive framework to benchmark state-of-the-art detectors systematically. By integrating diverse public datasets and baseline detection algorithms under a single, unified API, our toolkit facilitates rigorous and reproducible evaluation. DetectZoo provides reference implementations of 61 detectors, native loaders for 22 benchmark datasets, and a standardized evaluation pipeline that reports multiple metrics through a common interface. Each detector is self-contained yet accessible through the same interface, automatically caches pretrained weights, and reproduces the original published results. DetectZoo lowers the barrier to entry for multi-modal AI forensics, enabling researchers to identify performance gaps across domains and accelerating the development of robust, generalizable detection techniques. The open-source repository and comprehensive documentation are publicly available at https://github.com/sadjadeb/DetectZoo, and the package can be installed via pip install detectzoo.

翻译：生成式模型的日益普及与能力提升，正逐步模糊人类与机器生成内容之间的界限，从而推动了跨文本、图像及音频模态的检测研究不断增长。现有的大多数检测器要么是商业软件，要么即使开源，其代码库也存在定制化预处理、评估协议与评估指标不兼容的问题，这严重阻碍了其采用、公平比较与复现。为填补这一关键空白，我们提出了DetectZoo——作为首个可扩展工具包，旨在为文本、音频及图像模态的AI生成内容检测提供统一接口。DetectZoo对从数据摄入、预处理到模型评估的完整实证流程进行了标准化，为研究人员提供了系统化基准测试最先进检测器的统一框架。通过将多样化的公开数据集与基线检测算法整合至单一统一API中，该工具包实现了严格且可复现的评估。DetectZoo提供了61种检测器的参考实现、22个基准数据集的原生加载器，以及通过通用接口报告多项指标的标准化评估流程。每个检测器虽独立封装，却可通过同一接口访问，自动缓存预训练权重，并复现原始已发表结果。DetectZoo降低了多模态AI取证领域的准入门槛，助力研究者识别跨域性能差距，加速稳健、可泛化检测技术的发展。开源仓库及详细文档于https://github.com/sadjadeb/DetectZoo 公开提供，且可通过pip install detectzoo命令安装。