# Deepfake Detection of Occluded Images Using a Patch-based Approach

arXiv: https://arxiv.org/abs/2304.04537 · HTML: https://mlsanity.com/paper/2304.04537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Global Texture Enhancement for Fake Face Detection in the Wild](https://mlsanity.com/paper/2002.00133/) (2020)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [Detecting GAN-generated Imagery using Color Cues](https://mlsanity.com/paper/1812.08247/) (2018)
- [GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection](https://mlsanity.com/paper/1911.05351/) (2019)
- [Learning to Detect Fake Face Images in the Wild](https://mlsanity.com/paper/1809.08754/) (2018)
- [DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance](https://mlsanity.com/paper/2004.07532/) (2020)
- [Identification of Deep Network Generated Images Using Disparities in Color Components](https://mlsanity.com/paper/1808.07276/) (2018)
- [On Detecting GANs and Retouching based Synthetic Alterations](https://mlsanity.com/paper/1901.09237/) (2019)

## Abstract (arXiv)

DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems. Thus, detection of real or fake images is significant specially to authenticate originality of people's images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision. In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features. To test our approach, new datasets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as real images. The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4%-7.9% in the different built data-sets. Also, we have shown in experimental results that weighing the patches may improve accuracy.