Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
The rapid advancement of synthetic media, while beneficial, has also spawned GAN-generated deepfakes, which pose risks, including misinformation and digital fraud. This paper investigates the detectability of GAN-generated static images, focusing on residual artifacts that are imperceptible to human...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/923 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589216733724672 |
|---|---|
| author | Tamer Say Mustafa Alkan Aynur Kocak |
| author_facet | Tamer Say Mustafa Alkan Aynur Kocak |
| author_sort | Tamer Say |
| collection | DOAJ |
| description | The rapid advancement of synthetic media, while beneficial, has also spawned GAN-generated deepfakes, which pose risks, including misinformation and digital fraud. This paper investigates the detectability of GAN-generated static images, focusing on residual artifacts that are imperceptible to humans but detectable through digital analysis. Our approach introduces three key advancements: (1) a taxonomy for classifying GAN residues in deepfake detection; (2) a unique mixed dataset combining StyleGAN3, ProGAN, and InterfaceGAN to aid cross-model detection research; and (3) a combination of frequency space analysis and RGB color correlation methods to improve artifact detection. Covering three different transform methods, three GAN models, and twelve classification methods, ours is the most comprehensive study of detection of static deepfake face images produced by GANs. Our results demonstrate that artifact-based detection can achieve high accuracy, precision, recall, and F1 scores, challenging prior assumptions about the detectability of synthetic face images. |
| format | Article |
| id | doaj-art-c60f4161157647a4b838d022393b0447 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c60f4161157647a4b838d022393b04472025-01-24T13:21:21ZengMDPI AGApplied Sciences2076-34172025-01-0115292310.3390/app15020923Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact AnalysisTamer Say0Mustafa Alkan1Aynur Kocak2Department of Information Security Engineering, Graduate School of Natural and Applied Sciences, Gazi University, 06560 Ankara, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, 06560 Ankara, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, 06560 Ankara, TurkeyThe rapid advancement of synthetic media, while beneficial, has also spawned GAN-generated deepfakes, which pose risks, including misinformation and digital fraud. This paper investigates the detectability of GAN-generated static images, focusing on residual artifacts that are imperceptible to humans but detectable through digital analysis. Our approach introduces three key advancements: (1) a taxonomy for classifying GAN residues in deepfake detection; (2) a unique mixed dataset combining StyleGAN3, ProGAN, and InterfaceGAN to aid cross-model detection research; and (3) a combination of frequency space analysis and RGB color correlation methods to improve artifact detection. Covering three different transform methods, three GAN models, and twelve classification methods, ours is the most comprehensive study of detection of static deepfake face images produced by GANs. Our results demonstrate that artifact-based detection can achieve high accuracy, precision, recall, and F1 scores, challenging prior assumptions about the detectability of synthetic face images.https://www.mdpi.com/2076-3417/15/2/923computer visiondeepfakessynthetic mediamachine learning |
| spellingShingle | Tamer Say Mustafa Alkan Aynur Kocak Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis Applied Sciences computer vision deepfakes synthetic media machine learning |
| title | Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis |
| title_full | Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis |
| title_fullStr | Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis |
| title_full_unstemmed | Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis |
| title_short | Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis |
| title_sort | advancing gan deepfake detection mixed datasets and comprehensive artifact analysis |
| topic | computer vision deepfakes synthetic media machine learning |
| url | https://www.mdpi.com/2076-3417/15/2/923 |
| work_keys_str_mv | AT tamersay advancinggandeepfakedetectionmixeddatasetsandcomprehensiveartifactanalysis AT mustafaalkan advancinggandeepfakedetectionmixeddatasetsandcomprehensiveartifactanalysis AT aynurkocak advancinggandeepfakedetectionmixeddatasetsandcomprehensiveartifactanalysis |