Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks - DURU, ADİL DENİZ (My University, 2022)
© 2022 IEEE.Deep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content.
Keywords: Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Kontrol ve Sistem Mühendisliği; Sinyal İşleme; Bilgisayar Bilimleri; Algoritmalar; Yaşam Bilimleri; Temel Bilimler; Mühendislik ve Teknoloji; Information Systems, Communication and Control Engineering; Control and System Engineering; Signal Processing; Computer Sciences; algorithms; Life Sciences; Natural Sciences; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Yaşam Bilimleri (LIFE); Bilgisayar Bilimi; Mühendislik; Sinirbilim ve Davranış; OTOMASYON & KONTROL SİSTEMLERİ; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK; Engineering, Computing & Technology (ENG); Life Sciences (LIFE); COMPUTER SCIENCE; ENGINEERING; NEUROSCIENCE & BEHAVIOR; AUTOMATION & CONTROL SYSTEMS; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; ENGINEERING, ELECTRICAL & ELECTRONIC; Yapay Zeka; Fizik Bilimleri; Bilgisayar Bilimi Uygulamaları; Bilgisayarla Görme ve Örüntü Tanıma; Kontrol ve Optimizasyon; İnsan Bilgisayar Etkileşimi; Artificial Intelligence; Physical Sciences; Computer Science Applications; Computer Vision and Pattern Recognition; Control and Optimization; Human-Computer Interaction; Deep-fakes; face exploitation; AI; DL; autoencoders; generative adversarial network; forensics; review