DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities

DetectZoo is an open-source toolkit that standardizes AI-generated content detection across text, audio, and image modalities, providing 61 detector implementations and 22 benchmark datasets under a unified API. The project addresses fragmentation in the detection ecosystem by enabling reproducible evaluation and fair comparison of detection methods, lowering barriers for researchers developing robust generalization techniques.

DetectZoo tackles a fundamental infrastructure gap in AI forensics research. As generative models become increasingly sophisticated, the ability to reliably detect synthetic content has emerged as a critical challenge for content moderation, authentication, and trust infrastructure. The toolkit's primary contribution lies in standardization—previous detection work existed in siloed implementations with incompatible preprocessing, evaluation metrics, and codebases, making it nearly impossible to conduct fair comparative analysis or reproduce published results.

The fragmentation stemmed from detectors being distributed across commercial platforms and isolated academic repositories with bespoke requirements. This created a "tower of Babel" problem where researchers couldn't efficiently benchmark competing approaches or understand which methods generalized across domains. DetectZoo's unified interface, automating everything from data ingestion to metric reporting, directly addresses this bottleneck by providing a common reference implementation.

For the broader AI safety and content moderation ecosystem, this toolkit accelerates the development cycle for detection systems. By enabling easier cross-domain performance evaluation, researchers can identify critical gaps where detection fails—particularly important as synthetic content sophistication increases across text, audio, and visual domains. The reference implementations help demystify published methods while the standardized pipeline reduces implementation variance that often confounds comparative studies.

Looking forward, DetectZoo's infrastructure becomes increasingly valuable as detection becomes commoditized. The toolkit's extensibility suggests it could evolve into an industry standard, similar to how frameworks like Hugging Face standardized transformer model access. This positions it as foundational infrastructure for the emerging AI forensics sector.

• →DetectZoo integrates 61 detectors and 22 datasets under unified API, eliminating incompatible codebases that previously fragmented research.
• →Standardized evaluation pipeline enables fair performance comparison across text, image, and audio detection methods for the first time.
• →Open-source implementation automatically caches weights and reproduces published results, accelerating adoption and reducing research friction.
• →The toolkit addresses critical infrastructure gap in AI safety ecosystem as synthetic content detection becomes increasingly important for content moderation.
• →Extensible framework positions DetectZoo as potential industry standard for AI-generated content forensics research and evaluation.