Visual Chirality
This addresses the problem of understanding mirrored image distributions for researchers in computer vision, with implications for data augmentation, self-supervised learning, and image forensics, but it is incremental as it builds on known geometric chirality concepts.
The paper investigates how mirror reflections affect the statistics of visual data, termed 'visual chirality', and finds low-level chiral signals in imagery from camera processing and in images of people and faces.
How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual data are changed by reflection. We refer to these changes as "visual chirality", after the concept of geometric chirality - the notion of objects that are distinct from their mirror image. Our analysis of visual chirality reveals surprising results, including low-level chiral signals pervading imagery stemming from image processing in cameras, to the ability to discover visual chirality in images of people and faces. Our work has implications for data augmentation, self-supervised learning, and image forensics.