Derin Sahte ile Manipüle Edilmiş Yüz Görüntülerin Tespiti için YOLOV3 Tabanlı Bir Yöntem

Year 2024, Volume: 19 Issue: 2, 315 - 324, 30.09.2024

Mehmet Karaköse , Hasan Yetiş , Mert Çeçen

https://doi.org/10.55525/tjst.1386253

Abstract

Teknolojinin ilerlemesi ve görüntü, ses ve videoların sanal ortama aktarılmasını kolaylaştıran uygulamaların gelişmesiyle birlikte insanların kişisel bilgi, video ve görsellerine ulaşmak çok daha kolay hale gelmiştir. Derin sahte teknolojisi, derin öğrenme ve yapay zekâ tekniklerini kullanarak gerçek görüntü veya seslerin sahtelerini üretmek için kullanılmaktadır. Günümüzde eğlence ve film endüstrilerinde kullanılmasının yanı sıra, sahte haber oluşturma ve insanları itibarsızlaştırma gibi durumlarda da kullanılmaktadır. Bu durumların önüne geçmek için literatürde derin sahte görsel ve videoların tespitine yönelik farklı çalışmalar yapılmıştır. Bu çalışmada kapsamlı bir literatür taraması yapılmış ve farklı veri setlerinden veya videolardan gerçek ve sahte görseller toplanmış, etiketlenmiş ve gerekli ön işleme adımları uygulanarak bir veri seti oluşturulmuştur. Oluşturulan veri seti ile Evrişimli Sinir Ağlarını kullanarak geleneksel yöntemlerden farklı bir şekilde sınıf olasılıklarını hesaplayan ve tüm işlemleri tek bir regresyon probleminde ele alan hızlı ve yüksek doğrulukla tespit yapabilen YOLOv3 teknolojisi ile eğitim gerçekleştirilmiş ve modelleme süreci anlatılmıştır. Çalışmada yapılan testlerle derin sahte teknolojisiyle üretilmiş sahte görüntüleri %95 doğrulukla tespit edebilen bir model elde edilmiştir.

Keywords

Evrişimli sinir ağları, deepfake görüntü algılama, derin öğrenme, YOLOV3

Project Number

Tübitak 1005 - 122E676

A YOLOV3-Based Method for Detecting Deepfake Manipulated Facial Images

Abstract

With the advancement of technology and the development of applications that make it easier to transfer images, sounds and videos to the virtual environment, it has become much easier to access people's personal information, videos and images. Deepfake technology produces fakes of authentic images or sounds using deep learning and artificial intelligence techniques. Today, in addition to being used in the entertainment and film industries, it is also used in situations such as creating fake news and discrediting people. Different studies have been conducted in the literature to detect deepfake images and videos to prevent these situations. In this study, a comprehensive literature review was conducted. Real and fake images were collected and labelled from different datasets or videos, and a dataset was created by applying the necessary pre-processing steps. With the created dataset, training was carried out with YOLOv3 technology, which calculates class probabilities differently from traditional methods using Convolutional Neural Networks (CNN) and handles all operations in a single regression problem, which can make fast and high-accurate detection, and the modelling process is explained. With the tests performed in the study, the model that can detect fake images produced with deepfake technology with 95% accuracy was obtained.

Keywords

Convolutional neural networks, deepfake image detection, deep learning, YOLOV3

Supporting Institution

TUBİTAK

Project Number

Tübitak 1005 - 122E676

Thanks

This study was supported by the TUBİTAK (The Scientific and Technological Research Council of Turkey) under Grant No: 122E676.

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