Imagine an AI describing a scene, confidently pointing out details that simply aren't there—like a phantom bicycle in a park or a cat wearing a hat that doesn't exist. This isn't science fiction, it's a real problem called 'hallucination' that plagues today's vision-language models (VLMs). These powerful AIs, designed to understand and describe images, sometimes fabricate details, impacting their reliability. Researchers are tackling this challenge head-on, and a new method called FIHA offers a promising solution. FIHA acts like a meticulous fact-checker, automatically generating questions about images and their captions, covering objects, attributes (like color or size), and relationships between objects. It then quizzes the VLM to uncover inconsistencies and expose hallucinations without relying on expensive human annotations or other AI models. This process not only helps identify the presence of hallucinations but also categorizes them based on object, attribute, and relation types, organizing them into a tree-like structure called a 'Davidson Scene Graph.' This means if an AI misses a fundamental detail—like the presence of a car—all subsequent questions about its color or position become irrelevant and are flagged automatically. Researchers tested FIHA with several popular VLMs and found a wide range in performance, with larger models like GPT-4V performing the best. Importantly, FIHA identified hallucinations in even these top performers, highlighting the ongoing challenge. One key finding: Identifying relations between objects (e.g., "The person is standing next to the car") is the hardest task for VLMs, proving that context is crucial for a deeper understanding of images. FIHA's approach also shows promise for analyzing real-world images with varying levels of noise and distortion. The approach performed remarkably well on images with simulated fog, exhibiting the potential to handle more challenging datasets and improve reliability. As VLMs become increasingly integrated into our daily lives—from autonomous vehicles to content creation—FIHA and similar frameworks could play a crucial role in building trust by autonomously detecting and flagging these AI 'hallucinations.' The next step is to explore methods to not only identify these inaccuracies but to actively mitigate them, paving the way for more reliable and trustworthy AI-powered image understanding.