IllusionBench: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

We introduce **IllusionBench**, the first benchmark designed to evaluate the capability of Vision-Language Models in understanding **visual illusions**. **IllusionBench** is a large-scale visual illusion dataset, encompassing 1k+ images, 5k+ question-answer pairs, and 1k+ golden text descriptions, covering the presence, causes, and content of illusions. We benchmark the performance of ten state-of-the-art VLMs on this dataset, evaluating their understanding of illusion images through true-or-false, multiple-choice, and open-ended descriptive tasks. ## IllusionBench Construction **IllusionBench** includes 1K+ images from various online repositories. The detailed distribution is shown below. 1. **Classic Cognitive Illusion:** These include blur, distortion, paradox, and fictitious illusions—key examples of traditional synthetic illusions. Designed by psychologists, these ambiguous images test VLMs' alignment with human perception. 2. **Trap Illusion:** Trap illusions are edited versions of classic visual illusions, resembling them in appearance but differing in physical properties. 3. **Real Scene Illusion:** These images depict real-world objects and scenes, with unique and definite semantic descriptions. 4. **Ishihara Color Blindness Detection:** Ishihara images, verified by vision-healthy individuals, where the patterns convey unique and definite semantics. 5. **No Illusion:** Depicting diverse subjects such as people, landscapes, and objects.

Performance of advanced VLMs and human evaluators on IllusionBench+ perception tasks (left) and description tasks (right). The left image shows P1-P5 representing perception tasks on the subsets of **Classic Cognitive Illusion, Real Scene Illusion, No Illusion, Ishihara Image, and Trap Illusion**, respectively. Similarly, the right image shows D1-D5 representing description tasks on these subsets. "T/F", "Mul", "Sem", and "Illu" respectively represent true-or- false, multiple-choice, semantic descriptions, and illusion descriptions.

Assume that you have download the [**IllusionBench**](https://huggingface.co/datasets/MingZhangSJTU/IllusionBench) We provide a sample `Closed_inference.py` for testing Qwen-vl-max API format on **IllusionBench** true-or-false question and multiple-choice question. We provide a sample `Opened_inference.py` for testing Qwen-vl-max API format on **IllusionBench** semantic and illusion description question. You can modify the code to test your own model.