We’ve spent a lot of time talking about text hallucinations.
But image hallucination is a very different and often more dangerous problem.
In vision-language systems, hallucination isn’t about plausible lies.
It’s about inventing visual reality.
Examples:
- Describing people who aren’t there
- Assigning attributes that don’t exist
- Inferring actions that never happened
As these models are deployed for:
E-commerce product listings
Accessibility captions
Document extraction
Medical imaging workflows
…the cost of hallucination changes from “wrong answer” to “real-world consequence.”
The issue is that most evaluation pipelines are still text-first.
They score fluency, relevance, or similarity but never verify whether the image actually supports the description.
Image hallucination requires multimodal evaluation:
- Compare generated text against visual evidence
- Reason about object presence, attributes, and relationships
- Detect contradictions between image and output
This isn’t a niche problem.
It’s an emerging reliability gap as vision models move into production.
Curious how others are approaching hallucination detection for image-based systems.
