# GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

arXiv: https://arxiv.org/abs/1911.05351 · HTML: https://mlsanity.com/paper/1911.05351/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 30 of 42)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023)
- [Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024](https://mlsanity.com/paper/2503.02857/) (2025)
- [GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection](https://mlsanity.com/paper/2211.08615/) (2022)
- [FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data](https://mlsanity.com/paper/2311.10476/) (2023)
- [MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes](https://mlsanity.com/paper/2103.14211/) (2021)
- [Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack](https://mlsanity.com/paper/2203.11433/) (2022)
- [Exposing Lip-syncing Deepfakes from Mouth Inconsistencies](https://mlsanity.com/paper/2401.10113/) (2024)
- [AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection](https://mlsanity.com/paper/2312.08675/) (2023)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [Misleading Deep-Fake Detection with GAN Fingerprints](https://mlsanity.com/paper/2205.12543/) (2022)
- [Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)](https://mlsanity.com/paper/2508.11716/) (2025)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition](https://mlsanity.com/paper/2310.00359/) (2023)
- [Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline](https://mlsanity.com/paper/2412.19685/) (2024)
- [General GAN-generated image detection by data augmentation in fingerprint domain](https://mlsanity.com/paper/2212.13466/) (2022)
- [OTB-morph: One-Time Biometrics via Morphing](https://mlsanity.com/paper/2302.09053/) (2023)
- [Deep Fake Detection: Survey of Facial Manipulation Detection Solutions](https://mlsanity.com/paper/2106.12605/) (2021)
- [OTB-morph: One-Time Biometrics via Morphing applied to Face Templates](https://mlsanity.com/paper/2111.13213/) (2021)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors](https://mlsanity.com/paper/2210.03683/) (2022)
- [Anisotropic multiresolution analyses for deepfake detection](https://mlsanity.com/paper/2210.14874/) (2022)
- [FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection](https://mlsanity.com/paper/2504.07761/) (2025)
- [FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection](https://mlsanity.com/paper/2509.16602/) (2025)
- [Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation](https://mlsanity.com/paper/2211.03509/) (2022)
- [Deepfake Face Traceability with Disentangling Reversing Network](https://mlsanity.com/paper/2207.03666/) (2022)
- [PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics](https://mlsanity.com/paper/2310.02140/) (2023)

## Abstract (arXiv)

The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation. The main contributions of this study are four-fold: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images based on autoencoders is described, in order to spoof facial manipulation detection systems while keeping the visual quality of the resulting images; ii) an in-depth analysis of the recent literature in facial manipulation detection; iii) a complete experimental assessment of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task in unconstrained scenarios; and finally iv) we announce a novel public database, named iFakeFaceDB, yielding from the application of our proposed GAN-fingerprint Removal approach (GANprintR) to already very realistic synthetic fake images. The results obtained in our empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this study.