@inproceedings{e9d371d87e424099bb08d7a882f60346,
title = "Face Manipulation Detection via Auxiliary Supervision",
abstract = "The rapid progress of face manipulation technology has attracted people{\textquoteright}s attention. At present, a reliable edit detection algorithm is urgently needed to identify real and fake faces to ensure social credibility. Previous deep learning approaches formulate face manipulation detection as a binary classification problem. Many works struggle to focus on specific artifacts and generalize poorly. In this paper, we design reasonable auxiliary supervision to guide the network to learn discriminative and generalizable cues. A multi-scale framework is proposed to estimate the manipulation probability with texture map and blending boundary as auxiliary supervisions. These supervisions will guide the network to focus on the underlying texture information and blending boundary, making the learned features more generalized. Experiments on FaceForensics and FaceForensics++ datasets have demonstrated the effectiveness and generalization of our method.",
keywords = "Artifacts, Deepfake, Forgery detection",
author = "Xinyao Wang and Taiping Yao and Shouhong Ding and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 27th International Conference on Neural Information Processing, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",
year = "2020",
doi = "10.1007/978-3-030-63830-6\_27",
language = "英语",
isbn = "9783030638290",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "313--324",
editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, \{Andrew Chi-Sing\} and Kwok, \{James T.\} and Chan, \{Jonathan H.\} and Irwin King",
booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings",
address = "德国",
}