Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations
This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their mi...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/11/711 |
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| author | Vito Nicola Convertini Donato Impedovo Ugo Lopez Giuseppe Pirlo Gioacchino Sterlicchio |
| author_facet | Vito Nicola Convertini Donato Impedovo Ugo Lopez Giuseppe Pirlo Gioacchino Sterlicchio |
| author_sort | Vito Nicola Convertini |
| collection | DOAJ |
| description | This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats. |
| format | Article |
| id | doaj-art-8a4e026d96b940a09f0484b39949cbf7 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-8a4e026d96b940a09f0484b39949cbf72025-08-20T01:53:53ZengMDPI AGInformation2078-24892024-11-01151171110.3390/info15110711Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN CreationsVito Nicola Convertini0Donato Impedovo1Ugo Lopez2Giuseppe Pirlo3Gioacchino Sterlicchio4Department of Informatics, University of Bari Aldo Moro, 70125 Bari, ItalyDepartment of Informatics, University of Bari Aldo Moro, 70125 Bari, ItalyDepartment of Informatics, University of Bari Aldo Moro, 70125 Bari, ItalyDepartment of Informatics, University of Bari Aldo Moro, 70125 Bari, ItalyDepartment of Mechanics, Mathematics & Management, Polytechnic University of Bari, 70125 Bari, ItalyThis study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.https://www.mdpi.com/2078-2489/15/11/711Generative Adversarial Network (GAN)deepfake detectiondiscrete Fourier transform (DFT)face forgeryStyleGANspectrum analysis |
| spellingShingle | Vito Nicola Convertini Donato Impedovo Ugo Lopez Giuseppe Pirlo Gioacchino Sterlicchio Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations Information Generative Adversarial Network (GAN) deepfake detection discrete Fourier transform (DFT) face forgery StyleGAN spectrum analysis |
| title | Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations |
| title_full | Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations |
| title_fullStr | Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations |
| title_full_unstemmed | Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations |
| title_short | Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations |
| title_sort | discrete fourier transform in unmasking deepfake images a comparative study of stylegan creations |
| topic | Generative Adversarial Network (GAN) deepfake detection discrete Fourier transform (DFT) face forgery StyleGAN spectrum analysis |
| url | https://www.mdpi.com/2078-2489/15/11/711 |
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