# Fighting deepfakes by detecting GAN DCT anomalies

arXiv: https://arxiv.org/abs/2101.09781 · HTML: https://mlsanity.com/paper/2101.09781/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 30 of 31)

- [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)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [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)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Training behavior of deep neural network in frequency domain](https://mlsanity.com/paper/1807.01251/) (2018)
- [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019)
- [DeepFake Detection by Analyzing Convolutional Traces](https://mlsanity.com/paper/2004.10448/) (2020)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation](https://mlsanity.com/paper/1812.09912/) (2018)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [Detecting CNN-Generated Facial Images in Real-World Scenarios](https://mlsanity.com/paper/2005.05632/) (2020)
- [Fighting Deepfake by Exposing the Convolutional Traces on Images](https://mlsanity.com/paper/2008.04095/) (2020)
- [FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces](https://mlsanity.com/paper/1909.06122/) (2019)

## Built on by (most cited first, 30 of 39)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Combining EfficientNet and Vision Transformers for Video Deepfake Detection](https://mlsanity.com/paper/2107.02612/) (2021)
- [AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors](https://mlsanity.com/paper/2310.17419/) (2023)
- [Generalizable Synthetic Image Detection via Language-guided Contrastive Learning](https://mlsanity.com/paper/2305.13800/) (2023)
- [AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark](https://mlsanity.com/paper/2406.00783/) (2024)
- [Wavelet-Packets for Deepfake Image Analysis and Detection](https://mlsanity.com/paper/2106.09369/) (2021)
- [Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models](https://mlsanity.com/paper/2303.00608/) (2023)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [On the Exploitation of Deepfake Model Recognition](https://mlsanity.com/paper/2204.04513/) (2022)
- [Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models](https://mlsanity.com/paper/2501.07033/) (2025)
- [A Review of Deep Learning-based Approaches for Deepfake Content Detection](https://mlsanity.com/paper/2202.06095/) (2022)
- [GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions](https://mlsanity.com/paper/2312.16220/) (2023)
- [Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues](https://mlsanity.com/paper/2212.14629/) (2022)
- [On the Exploitation of DCT-Traces in the Generative-AI Domain](https://mlsanity.com/paper/2402.02209/) (2024)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Deepfake Media Forensics: State of the Art and Challenges Ahead](https://mlsanity.com/paper/2408.00388/) (2024)
- [Not with my name! Inferring artists' names of input strings employed by Diffusion Models](https://mlsanity.com/paper/2307.13527/) (2023)
- [Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection](https://mlsanity.com/paper/2403.13479/) (2024)
- [Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media](https://mlsanity.com/paper/2404.15601/) (2024)
- [Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images](https://mlsanity.com/paper/2203.09928/) (2022)
- [SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction](https://mlsanity.com/paper/2508.05691/) (2025)
- [Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM](https://mlsanity.com/paper/2302.10280/) (2023)
- [GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning](https://mlsanity.com/paper/2311.07075/) (2023)
- [Present and Future Generalization of Synthetic Image Detectors](https://mlsanity.com/paper/2409.14128/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Smudged Fingerprints: A Systematic Evaluation of the Robustness of AI Image Fingerprints](https://mlsanity.com/paper/2512.11771/) (2025)
- [Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis](https://mlsanity.com/paper/2310.11204/) (2023)
- [Innovative Methods for Non-Destructive Inspection of Handwritten Documents](https://mlsanity.com/paper/2310.11217/) (2023)
- [A Novel Dataset for Non-Destructive Inspection of Handwritten Documents](https://mlsanity.com/paper/2401.04448/) (2024)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)

## Abstract (arXiv)

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The BETA statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.