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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of DetectZoo, a unified toolkit for AI-generated content detection across text, audio, and image modalities. This toolkit addresses critical gaps in the current landscape of detection methods, promoting reproducibility and facilitating systematic benchmarking, which is essential for advancing research in AI forensics.
Methodology
The methodology presented in DetectZoo is robust and comprehensive, addressing the fragmentation in AI-generated content detection across multiple modalities (text, audio, and image). The authors provide a unified API that simplifies the benchmarking of various detection algorithms, which is a significant improvement over existing tools that often operate in isolation. The toolkit's design emphasizes reproducibility and accessibility, allowing researchers to easily integrate new detectors and datasets. The inclusion of a standardized evaluation pipeline and multiple performance metrics enhances its utility for the research community.
Results & evaluation
The experimental evaluation is thorough, with the authors conducting extensive benchmarking across 61 detection methods and 22 datasets. They validate their implementations against previously published results, demonstrating the effectiveness of their toolkit in reproducing empirical findings. The results highlight the varying performance of detection methods across different modalities and tasks, providing valuable insights into the strengths and weaknesses of current approaches.
Reproducibility
The paper places a strong emphasis on reproducibility, with detailed descriptions of the implementation process, including hyperparameters and configurations for each detector. The toolkit's automated management of pretrained weights and model components further supports reproducibility. The authors also provide comprehensive documentation, which is crucial for ensuring that other researchers can replicate their experiments and build upon their work.
Limitations
While DetectZoo offers a significant advancement in the field, it does have limitations. The toolkit currently focuses on evaluation rather than providing unified training pipelines for supervised detectors. Additionally, there is uneven coverage across modalities, with more robust support for text compared to audio. The reliance on external reference models for some detectors may also limit strict reproducibility. Furthermore, the static nature of benchmark datasets may not fully capture the evolving characteristics of newer generative models.
Broader impact
DetectZoo has the potential to greatly impact the field of AI-generated content detection by providing a standardized framework that facilitates rigorous and reproducible evaluation. This can lead to improved transparency and trust in AI-generated content, which is increasingly important in various applications, including journalism, law, and social media. However, the authors also acknowledge the dual-use nature of their toolkit, as it could be exploited by malicious actors to evade detection systems. The main contribution of this paper is the introduction of DetectZoo, a unified toolkit for AI-generated content detection across text, audio, and image modalities. This toolkit addresses critical gaps in the current landscape of detection methods, promoting reproducibility and facilitating systematic benchmarking, which is essential for advancing research in AI forensics.
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