Why Image Hallucination Is More Dangerous Than Text Hallucination

Source: Dev.to

Introduction

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

Impact Areas

• E‑commerce product listings
• Accessibility captions
• Document extraction
• Medical imaging workflows

In these contexts, the cost of hallucination shifts from a “wrong answer” to a real‑world consequence.

Evaluation Gap

Most evaluation pipelines remain text‑first. They score fluency, relevance, or similarity but never verify whether the image actually supports the description.

Multimodal Evaluation

• Compare generated text against visual evidence
• Reason about object presence, attributes, and relationships
• Detect contradictions between the image and the output

Conclusion

Image hallucination is not a niche problem; it represents an emerging reliability gap as vision models move into production. Developing robust multimodal evaluation methods is essential to mitigate real‑world risks.