# DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

arXiv: https://arxiv.org/abs/2004.07532 · HTML: https://mlsanity.com/paper/2004.07532/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [KoDF: A Large-scale Korean DeepFake Detection Dataset](https://mlsanity.com/paper/2103.10094/) (2021)
- [CORE: Consistent Representation Learning for Face Forgery Detection](https://mlsanity.com/paper/2206.02749/) (2022)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [DefakeHop: A Light-Weight High-Performance Deepfake Detector](https://mlsanity.com/paper/2103.06929/) (2021)
- [Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation](https://mlsanity.com/paper/2102.09603/) (2021)
- [DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2104.11507/) (2021)
- [Deepfake Video Detection with Spatiotemporal Dropout Transformer](https://mlsanity.com/paper/2207.06612/) (2022)
- [Exposing Lip-syncing Deepfakes from Mouth Inconsistencies](https://mlsanity.com/paper/2401.10113/) (2024)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistencies](https://mlsanity.com/paper/2504.01470/) (2025)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection](https://mlsanity.com/paper/2306.06881/) (2023)
- [Generalized Facial Manipulation Detection with Edge Region Feature Extraction](https://mlsanity.com/paper/2102.01381/) (2021)
- [Deepfake Detection of Face Images based on a Convolutional Neural Network](https://mlsanity.com/paper/2503.11389/) (2025)
- [Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs](https://mlsanity.com/paper/2401.04241/) (2024)
- [Deepfake Detection for Facial Images with Facemasks](https://mlsanity.com/paper/2202.11359/) (2022)
- [Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers](https://mlsanity.com/paper/2304.03698/) (2023)
- [Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines](https://mlsanity.com/paper/2206.00535/) (2022)
- [Deepfake Detection of Occluded Images Using a Patch-based Approach](https://mlsanity.com/paper/2304.04537/) (2023)
- [Detecting Face Synthesis Using a Concealed Fusion Model](https://mlsanity.com/paper/2401.04257/) (2024)
- [A Backbone Benchmarking Study on Self-supervised Learning as a Auxiliary Task with Texture-based Local Descriptors for Face Analysis](https://mlsanity.com/paper/2603.22190/) (2026)

## Abstract (arXiv)

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1st generation such as UADFV and FaceForensics++ up to the latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors.