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.

Fake online content is becoming harder to detect because modern AI systems can generate convincing text, images, audio, and videos. This paper introduces FakeWorld 1.0, a benchmark for testing whether AI systems can determine whether different types of media are AI-generated and whether the information they present is true. FakeWorld builds multiple modalities around the same event, so a system must check both the authenticity of the media and the truthfulness of the described content. These examples are presented in webpage-like formats, closer to how people encounter suspicious information online. We also introduce OmniCheck, a verification framework that breaks the problem into smaller checks and provides explanations for its decisions.