# On Detecting GANs and Retouching based Synthetic Alterations

arXiv: https://arxiv.org/abs/1901.09237 · HTML: https://mlsanity.com/paper/1901.09237/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

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

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Towards General Visual-Linguistic Face Forgery Detection](https://mlsanity.com/paper/2307.16545/) (2023)
- [On the Effect of Selfie Beautification Filters on Face Detection and Recognition](https://mlsanity.com/paper/2110.08934/) (2021)
- [A Dataless FaceSwap Detection Approach Using Synthetic Images](https://mlsanity.com/paper/2212.02571/) (2022)
- [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)
- [Deepfake Detection of Occluded Images Using a Patch-based Approach](https://mlsanity.com/paper/2304.04537/) (2023)
- [Towards General Visual-Linguistic Face Forgery Detection(V2)](https://mlsanity.com/paper/2502.20698/) (2025)

## Abstract (arXiv)

Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further, with advancements in Generative Adversarial Networks (GANs), now changing attributes and retouching have become very easy. Such synthetic alterations have adverse effect on face recognition algorithms. While researchers have proposed to detect image tampering, detecting GANs generated images has still not been explored. This paper proposes a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images. The algorithm yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset. It outperforms the previous state of the art which reported an accuracy of 87% on the database. For distinguishing between real images and images generated using GANs, the proposed algorithm yields an accuracy of 99.83%.