Quick take - A study by researchers from Jinan University and Xi’an Jiaotong-Liverpool University emphasizes the importance of high-quality datasets in AI development, critiques current dataset protection methods, and introduces a Forgery Watermark Generator to address vulnerabilities in dataset ownership verification.
Fast Facts
- A study by researchers from Jinan University and Xi’an Jiaotong-Liverpool University emphasizes the importance of high-quality datasets for AI advancement and highlights challenges in dataset construction.
- The authors propose using backdoor watermarks for dataset protection, but raise concerns about their reliability, particularly regarding forgery attacks.
- The study introduces a Forgery Watermark Generator (FW-Gen) that can create forged watermarks with statistical significance similar to original watermarks, questioning the effectiveness of current watermarking methods.
- A three-stage court judgment process is proposed to improve dataset ownership verification, suggesting the involvement of third-party judicial agencies for credibility.
- The research calls for the development of more secure watermarking techniques and highlights the need for further investigation into dataset protection strategies.
Study Highlights Importance of High-Quality Datasets in AI
A recent study conducted by a team of researchers from Jinan University and Xi’an Jiaotong-Liverpool University has highlighted the critical role of high-quality datasets in technological advancement, particularly in artificial intelligence (AI). The research was supported by the Research Development Fund, the top talent award project, and the National Natural Science Foundation of China.
Authors and Affiliations
The authors, Zhiying Li, Zhi Liu, Dongjie Liu, Shengda Zhuo, Guanggang Geng, Jian Weng, Shanxiang Lyu, and Xiaobo Jin, are affiliated with the College of Cyber Security at Jinan University, Guangzhou, and the School of Advanced Technology at Xi’an Jiaotong-Liverpool University, Suzhou. Shanxiang Lyu and Xiaobo Jin are the corresponding authors for this study.
Dataset Challenges and Proposed Solutions
The paper addresses the challenges associated with constructing datasets, which are often costly and time-consuming. Public datasets are particularly vulnerable to exploitation. To combat this, the authors propose the use of backdoor watermarks as a method for dataset protection. These watermarks aim to serve as proof of ownership and safeguard copyright. However, the paper raises questions about their reliability by examining them from an attacker’s perspective.
A significant finding of the study is the identification of vulnerabilities in the dataset ownership verification process, specifically highlighting concerns regarding forgery attacks. The authors introduce a Forgery Watermark Generator (FW-Gen), designed to create forged watermarks that can achieve statistical significance comparable to original watermarks in copyright verification tests. Extensive experiments indicate that these forged watermarks can effectively mimic original watermarks, casting doubt on the reliability of current backdoor watermarking methods.
Recommendations for Improved Dataset Protection
The research emphasizes the need for improved techniques to protect public datasets and critiques existing methods that primarily focus on private datasets. The authors redefine the backdoor watermarking issue as a dataset ownership verification (DOV) problem and propose a three-stage court judgment process involving evidence submission, trial, and judgment. This process aims to enhance the practical applicability of watermarking in real-world scenarios.
Additionally, the introduction of third-party judicial agencies is suggested to bolster the credibility of DOV results. The study discusses the potential for attackers to forge watermarks and present rebuttal evidence in court, highlighting the importance of the target label during the forgery process. The FW-Gen utilizes distillation learning to optimize parameters for generating forged watermarks that can activate backdoor behavior in models.
Furthermore, the research recommends strategies for dataset owners to mislead attackers and calls for the development of more secure and less detectable watermarking techniques. The related work section reviews existing backdoor attack methods and dataset protection strategies, while the threat model delineates the goals, abilities, and knowledge of both defenders and attackers.
The experimental setup encompasses datasets, watermarking methods, and evaluation metrics employed to assess the effectiveness of FW-Gen. Results from the study reveal that forged watermarks can achieve similar or even stronger statistical significance compared to original watermarks across various scenarios. An ablation analysis of loss functions is included to evaluate their impact on watermark generation and effectiveness, ultimately suggesting that current backdoor watermarking methods may not provide reliable copyright verification, indicating a pressing need for further research in this domain.
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