  Visual illusions play a significant role in understanding visual perception.
Current methods in understanding and evaluating visual illusions are mostly
deterministic filtering based approach and they evaluate on a handful of visual
illusions, and the conclusions therefore, are not generic. To this end, we
generate a large-scale dataset of 22,366 images (BRI3L: BRightness Illusion
Image dataset for Identification and Localization of illusory perception) of
the five types of brightness illusions and benchmark the dataset using
data-driven neural network based approaches. The dataset contains label
information - (1) whether a particular image is illusory/nonillusory, (2) the
segmentation mask of the illusory region of the image. Hence, both the
classification and segmentation task can be evaluated using this dataset. We
follow the standard psychophysical experiments involving human subjects to
validate the dataset. To the best of our knowledge, this is the first attempt
to develop a dataset of visual illusions and benchmark using data-driven
approach for illusion classification and localization. We consider five
well-studied types of brightness illusions: 1) Hermann grid, 2) Simultaneous
Brightness Contrast, 3) White illusion, 4) Grid illusion, and 5) Induced
Grating illusion. Benchmarking on the dataset achieves 99.56% accuracy in
illusion identification and 84.37% pixel accuracy in illusion localization. The
application of deep learning model, it is shown, also generalizes over unseen
brightness illusions like brightness assimilation to contrast transitions. We
also test the ability of state-of-theart diffusion models to generate
brightness illusions. We have provided all the code, dataset, instructions etc
in the github repo: https://github.com/aniket004/BRI3L
