# Camera Bias in a Fine Grained Classification Task

arXiv: https://arxiv.org/abs/2007.08574 · HTML: https://mlsanity.com/paper/2007.08574/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification](https://mlsanity.com/paper/2109.09818/) (2021)
- [How Quality Affects Deep Neural Networks in Fine-Grained Image Classification](https://mlsanity.com/paper/2405.05742/) (2024)
- [Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework](https://mlsanity.com/paper/2405.05853/) (2024)
- [An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation](https://mlsanity.com/paper/2409.09530/) (2024)

## Abstract (arXiv)

We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera. We show that models trained on a dataset with camera / label correlations do not generalize well to images in which those correlations are absent, nor to images from unencountered cameras. Furthermore, we investigate which visual features they are exploiting for camera recognition. Our experiments present evidence against the importance of global color statistics, lens deformation and chromatic aberration, and in favor of high frequency features, which may be introduced by image processing algorithms built into the cameras.