FakeWorld 1.0: An Omni-modal Benchmark for Fake Media and Content

The rapidly increasing realism of AI-generated media has intensified the spread of deceptive content and undermined public trust. Existing research largely treats this challenge along two separate axes: media authenticity, which assesses whether content is real or machine-generated, and content veracity, which evaluates semantic consistency and factual correctness. This separation overlooks how real-world deception jointly exploits both dimensions. In this work, we present FakeWorld 1.0, an omni-modal benchmark that unifies media authenticity and content veracity within a single evaluation framework. Along the media axis, FakeWorld spans text, audio, image, and video synthesis. Along the content axis, it systematically instantiates cross-modal semantic inconsistencies and factual errors. These two axes are jointly embedded in realistic web-based and streaming-style presentation scenarios, reflecting how multimodal deception is composed, contextualized, and delivered in practice. FakeWorld further provides explainable annotations in the form of per-instance rationales, enabling transparent and evidence-based analysis. Under a unified evaluation protocol, experiments on both open- and closed-source multimodal large language models (MLLMs) reveal fundamental capability limits and demonstrate FakeWorld’s effectiveness in exposing high-fidelity, mixed-source deception. Beyond the benchmark, we introduce OmniChecker, an agentic framwork that performs joint, explainable detection across both axes and produces evidence-backed diagnostic reports. We position FakeWorld 1.0 as a realistic stress test and a practical foundation for advancing scalable, explainable detection of fake multimodal content.