# Dissecting Image Crops

arXiv: https://arxiv.org/abs/2011.11831 · HTML: https://mlsanity.com/paper/2011.11831/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [RWN: Robust Watermarking Network for Image Cropping Localization](https://mlsanity.com/paper/2110.05687/) (2021)
- [Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models](https://mlsanity.com/paper/2310.08577/) (2023)
- [StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings](https://mlsanity.com/paper/2104.12290/) (2021)
- [Image Protection for Robust Cropping Localization and Recovery](https://mlsanity.com/paper/2206.02405/) (2022)

## Abstract (arXiv)

The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optics, lenses will leave behind certain clues, notably chromatic aberration and vignetting. Photographers also leave behind other clues relating to image aesthetics and scene composition. We study how to detect these traces, and investigate the impact that cropping has on the image distribution. While our aim is to dissect the fundamental impact of spatial crops, there are also a number of practical implications to our work, such as revealing faulty photojournalism and equipping neural network researchers with a better understanding of shortcut learning. Code is available at https://github.com/basilevh/dissecting-image-crops.