# Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder

arXiv: https://arxiv.org/abs/1709.07598 · HTML: https://mlsanity.com/paper/1709.07598/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [On the Effect of Selfie Beautification Filters on Face Detection and Recognition](https://mlsanity.com/paper/2110.08934/) (2021)
- [RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection](https://mlsanity.com/paper/2307.10642/) (2023)
- [Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching](https://mlsanity.com/paper/2103.03654/) (2021)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)
- [OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters](https://mlsanity.com/paper/2207.12319/) (2022)
- [Robust Face Liveness Detection for Biometric Authentication using Single Image](https://mlsanity.com/paper/2511.02645/) (2025)
- [Detection of Digital Facial Retouching utilizing Face Beauty Information](https://mlsanity.com/paper/2512.08397/) (2025)
- [MirrorPPR: Exemplar-Based Portrait Photo Retouching](https://mlsanity.com/paper/2606.29308/) (2026)

## Abstract (arXiv)

Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introduced algorithms to detect whether a face image is original or retouched. However, previous work on this topic has not considered whether or how accuracy of retouching detection varies with the demography of face images. In this paper, we introduce a new Multi-Demographic Retouched Faces (MDRF) dataset, which contains images belonging to two genders, male and female, and three ethnicities, Indian, Chinese, and Caucasian. Further, retouched images are created using two different retouching software packages. The second major contribution of this research is a novel semi-supervised autoencoder incorporating "subclass" information to improve classification. The proposed approach outperforms existing state-of-the-art detection algorithms for the task of generalized retouching detection. Experiments conducted with multiple combinations of ethnicities show that accuracy of retouching detection can vary greatly based on the demographics of the training and testing images.