Generative AI (GenAI) can generate high-resolution and complex content mimicking the creativity of humans, thereby benefiting industries such as gaming, entertainment, and product design. In recent times, AI-generated fake videos, commonly referred to as deepfakes, have become more commonplace and convincing. An additional deepfake technique, face warping, uses digital processing to noticeably distort shapes on a face. Tracking such warping in images and videos is crucial and preventing its use for destructive purposes. A technique is proposed for detecting and localizing face warped areas in video. The input video is extracted to perform various image pre-processing techniques that refine the video into a format that is more likely to classify the classes efficiently. Transfer learning is employed, and the pre-trained model is adopted to train using Convolutional Neural Network (CNN) with the source videos to identify face warping. Based on the experimental results, it was determined that the proposed model detects and localizes the warped areas of the face satisfactorily with an accuracy of 89.25%.
Deepfake Face Warping Transfer Learning Convolutional Neural Network Generative Artificial Intelligence
National Institute of Technology, Tiruchirappalli, india
Generative AI (GenAI) can generate high-resolution and complex content mimicking the creativity of humans, thereby benefiting industries such as gaming, entertainment, and product design. In recent times, AI-generated fake videos, commonly referred to as deepfakes, have become more commonplace and convincing. An additional deepfake technique, face warping, uses digital processing to noticeably distort shapes on a face. Tracking such warping in images and videos is crucial and preventing its use for destructive purposes. A technique is proposed for detecting and localizing face warped areas in video. The input video is extracted to perform various image pre-processing techniques that refine the video into a format that is more likely to classify the classes efficiently. Transfer learning is employed, and the pre-trained model is adopted to train using Convolutional Neural Network (CNN) with the source videos to identify face warping. Based on the experimental results, it was determined that the proposed model detects and localizes the warped areas of the face satisfactorily with an accuracy of 89.25%.
Deepfake Face Warping Transfer Learning Convolutional Neural Network Generative Artificial Intelligence
Birincil Dil | İngilizce |
---|---|
Konular | Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer) |
Bölüm | Research Articles |
Yazarlar | |
Erken Görünüm Tarihi | 14 Aralık 2023 |
Yayımlanma Tarihi | |
Gönderilme Tarihi | 30 Ağustos 2023 |
Yayımlandığı Sayı | Yıl 2024 Cilt: 4 Sayı: 1 |
Journal of Metaverse
is indexed and abstracted by
Scopus and DOAJ
Publisher
Izmir Academy Association
www.izmirakademi.org