Survey on adversarial attacks and defense of face forgery and detection

Shiyu HUANG; Feng YE; Tianqiang HUANG; Wei LI; Liqing HUANG; Haifeng LUO

doi:10.11959/j.issn.2096-109x.2023049

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Survey on adversarial attacks and defense of face forgery and detection

Comprehensive Review | 更新时间：2024-06-03

- Survey on adversarial attacks and defense of face forgery and detection
- Chinese Journal of Network and Information Security Vol. 9, Issue 4, Pages: 1-15(2023)
- 作者机构：
  
  1. 福建师范大学计算机与网络空间安全学院，福建福州 350117
  2. 数字福建大数据安全技术研究所，福建福州 350117
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023049
  CLC： TP393
- Online First：2023-08，
  
  Published：25 August 2023
- 稿件说明：
移动端阅览
Shiyu HUANG, Feng YE, Tianqiang HUANG, et al. Survey on adversarial attacks and defense of face forgery and detection[J]. Chinese Journal of Network and Information Security, 2023, 9(4): 1-15.
DOI：

Shiyu HUANG, Feng YE, Tianqiang HUANG, et al. Survey on adversarial attacks and defense of face forgery and detection[J]. Chinese Journal of Network and Information Security, 2023, 9(4): 1-15. DOI： 10.11959/j.issn.2096-109x.2023049.

摘要

人脸伪造和检测是当前的研究热点。通过人脸伪造方法可以制作虚假人脸图像和视频，一些出于恶意目的而将名人虚假视频在社交网络上广泛传播，不仅侵犯了受害者的声誉，而且造成了不良的社会影响，因此需要开发对应的检测方法用于鉴别虚假视频。近年来，深度学习技术的发展与应用降低了人脸伪造与检测的难度。基于深度学习的人脸伪造方法能生成看起来更加真实的人脸，而基于深度学习的虚假人脸检测方法比传统方法具有更高的准确度。大量研究表明，深度学习模型容易受到对抗样本的影响而导致性能下降。近来在人脸伪造与检测的领域中，出现了一些利用对抗样本进行博弈的工作。原先的博弈模式变得更加复杂，伪造方和检测方在原先方法的基础上，都需要更多考虑对抗安全性。将深度学习方法和对抗样本相结合，是该研究领域未来的趋势。专注于对人脸伪造与检测中的对抗攻防这一领域进行综述。介绍人脸伪造与检测的概念以及目前主流的方法；回顾经典的对抗攻击和防御方法。阐述对抗攻击和防御方法在人脸伪造和检测上的应用，分析目前的研究趋势；总结对抗攻防对人脸伪造和检测带来的挑战，并讨论未来发展方向。

Abstract

Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos

often targeting celebrities

are widely circulated on social networks

damaging the reputation of victims and causing significant social harm.As a result

it is crucial to develop effective detection methods to identify fake videos.In recent years

deep learning technology has made the task of face forgery and detection more accessible.Deep learning-based face forgery methods can generate highly realistic faces

while deep learning-based fake face detection methods demonstrate higher accuracy compared to traditional approaches.However

it has been shown that deep learning models are vulnerable to adversarial examples

which can lead to a degradation in performance.Consequently

games involving adversarial examples have emerged in the field of face forgery and detection

adding complexity to the original task.Both fakers and detectors now need to consider the adversarial security aspect of their methods.The combination of deep learning methods and adversarial examples is thus the future trend in this research field

particularly with a focus on adversarial attack and defense in face forgery and detection.The concept of face forgery and detection and the current mainstream methods were introduced.Classic adversarial attack and defense methods were reviewed.The application of adversarial attack and defense methods in face forgery and detection was described

and the current research trends were analyzed.Moreover

the challenges of adversarial attack and defense for face forgery and detection were summarized

and future development directions were discussed.

关键词

Keywords

references

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