MedFakeDet: multi-organ medical deepfake detection and inpainting localization system via effective deep learning models and density maps
Yazarlar (1)
Arş. Gör. Mert ÇEÇEN Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Softwarex (Q2) | ||
| Dergi ISSN | 2352-7110 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2026 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-06-2026 |
| Cilt / Sayı / Sayfa | 34 / 1 / – | DOI | 10.1016/j.softx.2026.102668 |
| UAK Araştırma Alanları |
Görüntü İşleme
Yapay Zeka
Makine Öğrenmesi
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| Özet |
| The ability of deepfake technologies to produce highly realistic images has become a significant security concern for healthcare systems and insurance auditing processes, either through the generation of fake medical images or the realistic manipulation of original ones. This study employs effective deep learning methods to detect medical images generated by deepfake technologies and to localize inpainting-based manipulations. Two different datasets were constructed for this purpose: the MedFake dataset, which contains brain, lung, kidney, and chest images generated using multiple synthesis approaches; and the MedFakeInpaint dataset, which includes localized inpainting manipulations applied to brain images. For deepfake synthesis detection, an 8-class ResNet-based model was trained to jointly classify the organ type of a medical image and its real or fake status. For manipulation localization, a U-Net … |
| Anahtar Kelimeler |
| Medical deepfake detection; Tumor inpaint detection; Synthesis detection; UNET |
Science Direct
MedFakeDet: multi-organ medical deepfake detection and inpainting localization system via effective deep learning models and density maps
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |