The face deepfake detection challenge
Open accessAbstract
Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an “in the wild” scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and “minimum average distance to Manhattan” (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams’ proposed methods with corresponding ranking is presented in this paper.
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Metrics such as AUC and EER are essential for fully assessing a model's generalization across a wide range of techniques.
“As emphasized in [15], metrics such as the area under the curve (AUC) and error-efficiency ratio (EER) are essential for fully assessing a model's generalization across a wide range of techniques.”
From Beyond Accuracy: A Deployment-Oriented Benchmark of Deepfake Detection Models (2025) · cites this paper as [15]
Section 4.1 reports an AUC for one detector, and Section 5 says Equal Error Rate (EER) was used to set another detector’s classification threshold. The paper does not say these metrics are essential for fully assessing generalization across techniques. It also expands EER as “Equal Error Rate,” not “error-efficiency ratio”; page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe Deepfake Challenge dataset combines authentic CelebA and FFHQ images with GAN-generated manipulations (StarGAN, AttGAN, StyleGAN) for attribute/face synthesis.
“The Deepfake Challenge dataset [23] combines authentic CelebA and FFHQ images with GAN-generated manipulations (StarGAN, AttGAN, StyleGAN) for attribute/face synthesis.”
From Beyond Accuracy: A Deployment-Oriented Benchmark of Deepfake Detection Models (2025) · cites this paper as [23]
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Self-citation
- Authors
- Guarnera, L., Giudice, O., Guarnera, F., Ortis, A., Puglisi, G., Paratore, A., Bui, L. M. Q., Fontani, M., Coccomini, D. A., Caldelli, R., Falchi, F., Gennaro, C., Messina, N., Amato, G., Perelli, G., Concas, S., Cuccu, C., Orrù, G., Marcialis, G. L., & Battiato, S.
- Affiliations
- University of Catania (IT)
- Bank of Italy (IT)
- University of Cagliari (IT)
- iCTLab s.r.l., 95125 Catania, Italy
- Amped Software, 34149 Trieste, Italy
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (IT)
- Corresponding author
- Guarnera, Luca
- ORCID
- Guarnera, Luca
- Giudice, Oliver
- Guarnera, Francesco
- Ortis, Alessandro
- Puglisi, Giovanni
- Paratore, Antonino
- Bui, Linh M. Q.
- Fontani, Marco
- Coccomini, Davide Alessandro
- Caldelli, Roberto
- Falchi, Fabrizio
- Gennaro, Claudio
- Messina, Nicola
- Amato, Giuseppe
- Perelli, Gianpaolo
- Concas, Sara
- Cuccu, Carlo
- Orrù, Giulia
- Marcialis, Gian Luca
- Battiato, Sebastiano
- Venue
- Journal of Imaging
- Publisher
- MDPI AG
- Published
- 2022-09-28
- Volume
- 8
- Issue
- 10
- Pages
- 263
- ISSN
- 2313-433X (ISSN-L)
- PMC
- PMC9605671
- OpenAlex
- W4297477523
- Cited by
- 79
- References
- 47
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 14 · 2023: 19
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ISTI Open Portal (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, DFDC, EfficientNet, CelebA, FFHQ, StarGAN, StyleGAN
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