# Detection of Digital Facial Retouching utilizing Face Beauty Information

arXiv: https://arxiv.org/abs/2512.08397 · HTML: https://mlsanity.com/paper/2512.08397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [ObjectFormer for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2203.14681/) (2022)
- [GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection](https://mlsanity.com/paper/1911.05351/) (2019)
- [SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction](https://mlsanity.com/paper/1801.06345/) (2018)
- [Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder](https://mlsanity.com/paper/1709.07598/) (2017)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level](https://mlsanity.com/paper/1902.02593/) (2019)
- [Transferring Rich Deep Features for Facial Beauty Prediction](https://mlsanity.com/paper/1803.07253/) (2018)

## Abstract (arXiv)

Facial retouching to beautify images is widely spread in social media, advertisements, and it is even applied in professional photo studios to let individuals appear younger, remove wrinkles and skin impurities. Generally speaking, this is done to enhance beauty. This is not a problem itself, but when retouched images are used as biometric samples and enrolled in a biometric system, it is one. Since previous work has proven facial retouching to be a challenge for face recognition systems,the detection of facial retouching becomes increasingly necessary. This work proposes to study and analyze changes in beauty assessment algorithms of retouched images, assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection, and evaluates whether face beauty can be exploited to enhance the detection rate. In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.