The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence

The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the courts, where decisions increasingly depend on the authenticity of evidence such as receipts, communications, and administrative records. Unlike social media or academic settings, evidentiary documents are often only subtly altered, with small, localized edits that preserve overall plausibility while changing legal meaning. Yet progress on automated detection remains limited, largely due to the absence of suitable training and evaluation data especially suited for the justice system requirements. Existing resources are either focused on photos of human faces or natural scenery or on narrowly scoped academic or social media document types, and do not capture the structure, diversity, or manipulation patterns characteristic of real-world evidentiary data. As a result, current detection systems do not necessarily learn meaningful signals appropriate for the justice system. We introduce the CIFAR Synthetic Evidence Corpus, a dataset designed to enable rigorous evaluation of evidence verification under realistic and controlled conditions. The corpus spans multiple document families and a spectrum of manipulation strategies, from small field-level edits to complete document fabrication, and is constructed using a diverse set of state-of-the-art generative tools. It is organized to systematically vary both manipulation complexity and generation method, while enforcing source-level separation between training and test data to reflect real-world generalization challenges.

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

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.