@inproceedings{6eaacaf81afa4118856de6881cf747c7,
title = "Generalized Deepfake Detection Algorithm Based on Inconsistency Between Inner and Outer Faces",
abstract = "Deepfake refers to using artificial intelligence (AI) and machine learning techniques to create compelling and realistic media content, such as videos, images, or recordings, that appear real but are fake. The most common form of deepfake involves using deep neural networks to replace or superimpose faces in existing videos or images on top of other people{\textquoteright}s faces. While this technology can be used for various benign purposes, such as filmmaking or online education, it can also be used maliciously to spread misinformation by creating fake videos or images. Based on the classic deepfake generation process, this paper explores the Inconsistency between inner and outer faces in fake content to find synthetic defects and proposes a general deepfake detection algorithm. Experimental results show that our proposed method has certain advantages, especially regarding cross-method detection performance.",
keywords = "deepfake detection, generalization, manipulations",
author = "Jie Gao and Sara Concas and Giulia Orr{\`u} and Xiaoyi Feng and Marcialis, \{Gian Luca\} and Fabio Roli",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 22nd International Conference on Image Analysis and Processing, ICIAP 2023 ; Conference date: 11-09-2023 Through 15-09-2023",
year = "2024",
doi = "10.1007/978-3-031-51023-6\_29",
language = "英语",
isbn = "9783031510229",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "343--355",
editor = "Foresti, \{Gian Luca\} and Andrea Fusiello and Edwin Hancock",
booktitle = "Image Analysis and Processing - ICIAP 2023 Workshops, Proceedings",
}