# Preliminary Forensics Analysis of DeepFake Images

arXiv: https://arxiv.org/abs/2004.12626 · HTML: https://mlsanity.com/paper/2004.12626/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [Misleading Deep-Fake Detection with GAN Fingerprints](https://mlsanity.com/paper/2205.12543/) (2022)
- [GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions](https://mlsanity.com/paper/2312.16220/) (2023)
- [On the Exploitation of DCT-Traces in the Generative-AI Domain](https://mlsanity.com/paper/2402.02209/) (2024)
- [XAI-Based Detection of Adversarial Attacks on Deepfake Detectors](https://mlsanity.com/paper/2403.02955/) (2024)
- [Deepfake Media Forensics: State of the Art and Challenges Ahead](https://mlsanity.com/paper/2408.00388/) (2024)
- [DeepFeatureX Net: Deep Features eXtractors based Network for discriminating synthetic from real images](https://mlsanity.com/paper/2404.15697/) (2024)
- [Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images](https://mlsanity.com/paper/2203.09928/) (2022)
- [Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis](https://mlsanity.com/paper/2310.11204/) (2023)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [Detecting Face Synthesis Using a Concealed Fusion Model](https://mlsanity.com/paper/2401.04257/) (2024)

## Abstract (arXiv)

One of the most terrifying phenomenon nowadays is the DeepFake: the possibility to automatically replace a person's face in images and videos by exploiting algorithms based on deep learning. This paper will present a brief overview of technologies able to produce DeepFake images of faces. A forensics analysis of those images with standard methods will be presented: not surprisingly state of the art techniques are not completely able to detect the fakeness. To solve this, a preliminary idea on how to fight DeepFake images of faces will be presented by analysing anomalies in the frequency domain.