{"ID":3053239,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T20:41:05.129451022Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04205","arxiv_id":"2606.04205","title":"DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities","abstract":"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.","short_abstract":"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 wi...","url_abs":"https://arxiv.org/abs/2606.04205","url_pdf":"https://arxiv.org/pdf/2606.04205v1","authors":"[\"Sajad Ebrahimi\",\"Nima Jamali\",\"Bardia Shirsalimian\",\"Kelly McConvey\",\"Wentao Zhang\",\"Jalehsadat Mahdavimoghaddam\",\"Maksym Taranukhin\",\"Maura Grossman\",\"Vered Shwartz\",\"Yuntian Deng\",\"Ebrahim Bagheri\"]","published":"2026-06-02T20:49:20Z","proceeding":"cs.MM","tasks":"[\"cs.MM\",\"cs.AI\",\"cs.CL\",\"cs.CV\",\"cs.LG\",\"cs.SD\"]","methods":"[]","has_code":false,"code_links":[{"ID":612803,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-04T04:41:36.695875263Z","DeletedAt":null,"paper_id":3053239,"paper_url":"https://arxiv.org/abs/2606.04205","paper_title":"DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities","repo_url":"https://github.com/sadjadeb/DetectZoo","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}