Face Forgery Detection Technology Based on Deep Learning
DOI:
https://doi.org/10.54097/2q6k0q74Keywords:
Deep Learning, Face Forgery, Detection Technology.Abstract
The rapid evolution of generative artificial intelligence has significantly transformed the way image and video content is produced. Deepfake facial recognition, a prominent example, has become a research hotspot in computer vision and multimedia security. However, its potential for widespread application also carries the risk of potential misuse, sparking continued attention from both academia and industry. This paper systematically reviews the face forgery detection technology based on deep learning. With the development of artificial intelligence technologies such as Generative Adversarial Networks (GAN) and diffusion models, face forgery content has brought about serious security and ethical issues while bringing application possibilities. Traditional image forensics methods rely on manual features and have limited generalization ability, while deep learning significantly improves detection performance through end-to-end feature learning. This paper reviews and analyzes the principles and characteristics of mainstream detection methods from the perspectives of physiological signal features, image tampering traces, GAN generation features and image-level learning strategies, and summarizes the challenges that the current models still face in terms of generalization ability and anti-interference ability. Finally, the future research directions such as cross-domain detection framework, multi-modal fusion and active defense are proposed.
Downloads
References
[1] Yang X, Li Y, Qi H, Lyu S. Exposing GAN-synthesized faces using landmark locations. In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, 2019: 113 - 118.
[2] Li Y, Chang MC, Lyu S. In Ictu Oculi: Exposing AI created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 2018: 1 - 7.
[3] Zhu Xintong, Tang Yunqi, Geng Pengzhi. Detection algorithm of tamper and deepfake image based on feature fusion. Netinfo Security, 2021, 21 (8): 70 - 81
[4] Matern F, Riess C, Stamminger M. Exploiting visual artifacts to expose deepfakes and face manipulations. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 2019. DOI: 10.1109/WACVW.2019.00020.
[5] Yang Shaocong, Wang Jian, Sun Yunlian, et al. multi-level feature global consistency for forged face detection. Journal of Image and Graphics, 2022, 27 (9): 13.
[6] Qu Zuomin, Yin Qilin, Sheng Ziqi, et al. Review of active defense techniques for deepfake faces. Journal of Image and Graphics, 2024, 29 (2): 318 - 342.
[7] Li H, Li B, Tan S, et al. Identification of deep network generated images using disparities in color components. Signal Processing, 2020, 174: 107616.
[8] Nataraj L, Mohammed TM, Chandrasekaran S, et al. Detecting GAN generated fake images using co-occurrence matrices. arXiv preprint arXiv: 1903.06836, 2019.
[9] Afchar D, Nozick V, Yamagishi J, Echizen I. MesoNet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 2018: 1 - 7.
[10] Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M. Face Forensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 1 - 11.
[11] Chollet F. Xception: deep learning with depth wise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251 - 1258.
[12] Songsri-in K, Zafeiriou S. Complement face forensic detection and localization with facial landmarks. arXiv preprint arXiv: 1910.05455, 2019.
[13] Nguyen HH, Yamagishi J, Echizen I. Capsule-Forensics: using capsule networks to detect forged images and videos. In: ICASSP 2019 — 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019: 2307 - 2311.
[14] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556, 2014.
[15] Mo H, Chen B, Luo W. Fake faces identification via convolutional neural network. In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, 2018: 43 - 47.
[16] Cozzolino D, Thies J, Rössler A, Riess C, Nießner M, Verdoliva L. Forensic Transfer: weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510, 2018.
[17] Nguyen HH, Fang F, Yamagishi J, et al. multi-task learning for detecting and segmenting manipulated facial images and videos. In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2019: 1 - 8.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
