# Image Distortion Detection using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1805.10881 · HTML: https://mlsanity.com/paper/1805.10881/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

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

- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network](https://mlsanity.com/paper/2009.14563/) (2020)
- [Helping Visually Impaired People Take Better Quality Pictures](https://mlsanity.com/paper/2305.08066/) (2023)
- [DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions](https://mlsanity.com/paper/2407.16302/) (2024)
- [Predicting the Reliability of an Image Classifier under Image Distortion](https://mlsanity.com/paper/2412.16881/) (2024)

## Abstract (arXiv)

Image distortion classification and detection is an important task in many applications. For example when compressing images, if we know the exact location of the distortion, then it is possible to re-compress images by adjusting the local compression level dynamically. In this paper, we address the problem of detecting the distortion region and classifying the distortion type of a given image. We show that our model significantly outperforms the state-of-the-art distortion classifier, and report accurate detection results for the first time. We expect that such results prove the usefulness of our approach in many potential applications such as image compression or distortion restoration.