This is the616th original article. Your attention is the greatest encouragement to us!
Image source:
https://blog.itpub.net/31553577/viewspace-2215200/
The cover paper “Overview of Deepfake Proactive Defense Techniques” in the 2024 issue 2 of the Journal of Image and Graphics provides a comprehensive summary of current proactive defense techniques against deepfakes, with contributions fromProfessor Lu Wei’s team from Sun Yat-sen University.
Leave a message【Deepfake】in the background of the WeChat public account of the Journal of Image and Graphics to download the full text of the “Digital Media Deepfake and Defense” column
Citation Format:
Qu Zuomin, Yin Qilin, Sheng Ziqi, Wu Junyan, Zhang Bolin, Yu Shangrong, Lu Wei. 2024. Overview of Deepfake proactive defense techniques. Journal of Image and Graphics, 29(02):0318-0342
Qu Zuomin, Yin Qilin, Sheng Ziqi, Wu Junyan, Zhang Bolin, Yu Shangrong, Lu Wei. 2024. Overview of Deepfake proactive defense techniques. Journal of Image and Graphics, 29(02):0318-0342
[DOI:10.11834/jig.230128]
http://www.cjig.cn/jig/article/html/230128
Keywords:Deepfake; Defense against Deepfake; Proactive Defense; Adversarial Attack; Generative Adversarial Network (GAN); Deep Learning
1) A systematic summary of existing proactive defense methods against deepfake, including the classification of different defense algorithms, destruction targets, advantages and disadvantages, and robustness performance, along with links to open-source algorithm codes;
2) An introduction to the evaluation metrics and commonly used datasets in existing proactive defense technology papers, with links to publicly available datasets;
3) An explanation of the technical challenges and application challenges faced by proactive defense against deepfakes, with a discussion of future development prospects.
Deepfake proactive defense techniques can be summarized as: adding a certain degree of perturbation or watermark information to images or videos containing faces before users publish them on public internet platforms, thereby disrupting the results of malicious users using these facial materials for deepfake, making it easy for human observers to detect anomalies in these forged faces and reducing their credibility; or even if malicious users can achieve indistinguishable forgeries, effective tracing or authenticity verification can still be conducted after the forged images or videos are published, thus achieving the goal of “preemptive defense”.
Fig: Classification diagram of deepfake proactive defense techniques
Table: Comparison of defense effects of typical proactive interference defense methods
Table: Open-source algorithms for deepfake proactive defense techniques
Table: Open-source algorithms for deepfake
Table: Common datasets and links for deepfake proactive defense techniques
Challenges Faced by Proactive Defense Techniques:
1) Due to the inherent fragility of adversarial perturbations, proactive defense against deepfakes can easily be circumvented by adversarial sample detectors and defense algorithms, and some evasion algorithms targeting proactive defense models have also been proposed;
2) The weak black-box performance of proactive defense methods and the increasing training costs of cross-model watermarks limit their practicality.
FutureWork:
1) Research on defense methods with stronger robustness and black-box generalization performance to promote the research and application of proactive defense against deepfakes in real scenarios;
2) Research on proactive defense methods with high visual fidelity to maintain the visual perceptual quality of protected images and videos as much as possible.
Qu Zuomin, a master’s student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and AI generation and adversarial methods.
E-mail: [email protected]
Lu Wei, corresponding author, professor at the School of Computer Science, Sun Yat-sen University, editorial board member of the Journal of Image and Graphics, mainly researching AI generation and adversarial methods, digital forensics, and information hiding.
E-mail: [email protected]
Yin Qilin, a PhD student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and digital forensics.
E-mail: [email protected]
Sheng Ziqi, a PhD student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and digital forensics.
E-mail: [email protected]
Wu Junyan, a PhD student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and digital forensics.
E-mail: [email protected]
Zhang Bolin, a master’s student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and digital forensics.
E-mail: [email protected]
Yu Shangrong, a master’s student at the School of Computer Science, Sun Yat-sen University, mainly researching multimedia content security and public opinion analysis.
E-mail: [email protected]
Special Call for Papers | Text and Multimodal Large Models
Special Call for Papers | Cutting-edge AI Technologies for Autonomous Driving
Special Call for Papers | Applications of Image Graphics in Aerospace Vehicles
2024 Back-to-School Gift Package | 8 Latest Reviews on Object Detection
Paper Collection | 20 Reviews and Algorithm Papers on Transformer
Paper Collection | 9 Latest Reviews on Multimodal
Conference Collection | Image Graphics Academic Conferences from April to June 2024
Click to Read the Latest Issue of the Journal of Image and Graphics
This article is an exclusive piece for the Journal of Image and Graphics
Content is for learning and communication only
Copyright belongs to the original author
We welcome everyone to follow and share!
Editor: Xiu Xiu
Reviewer: Wutong Jun
Statement
We welcome the sharing of original content from this account; any media or institution may not reproduce or excerpt without authorization. For authorization, please leave a message in the background with “Organization Name + Article Title + Reprint/Forward” to contact us. Reproduction must indicate the original author and source as “Journal of Image and Graphics.” The purpose of reproducing this information is for dissemination and communication, and the content reflects the author’s views, not the position of this account. Please do not reprint without permission. If there are any issues regarding text, images, copyright, or other matters, please contact us within 20 days of the article’s publication, and we will handle it promptly. The Journal of Image and Graphics has the final interpretation right.
