LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large
  Multimodal Models
Authors: name: Junyan Ye, name: Baichuan Zhou, name: Zilong Huang, name: Junan Zhang, name: Tianyi Bai, name: Hengrui Kang, name: Jun He, name: Honglin Lin, name: Zihao Wang, name: Tong Wu, name: Zhizheng Wu, name: Yiping Chen, name: Dahua Lin, name: Conghui He, name: Weijia Li

With the rapid development of AI-generated content, the future internet may
be inundated with synthetic data, making the discrimination of authentic and
credible multimodal data increasingly challenging. Synthetic data detection has
thus garnered widespread attention, and the performance of large multimodal
models (LMMs) in this task has attracted significant interest. LMMs can provide
natural language explanations for their authenticity judgments, enhancing the
explainability of synthetic content detection. Simultaneously, the task of
distinguishing between real and synthetic data effectively tests the
perception, knowledge, and reasoning capabilities of LMMs. In response, we
introduce LOKI, a novel benchmark designed to evaluate the ability of LMMs to
detect synthetic data across multiple modalities. LOKI encompasses video,
image, 3D, text, and audio modalities, comprising 18K carefully curated
questions across 26 subcategories with clear difficulty levels. The benchmark
includes coarse-grained judgment and multiple-choice questions, as well as
fine-grained anomaly selection and explanation tasks, allowing for a
comprehensive analysis of LMMs. We evaluated 22 open-source LMMs and 6
closed-source models on LOKI, highlighting their potential as synthetic data
detectors and also revealing some limitations in the development of LMM
capabilities. More information about LOKI can be found at
https://opendatalab.github.io/LOKI/