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Security and Privacy on Generative Data in AIGC: A Survey (2309.09435v3)

Published 18 Sep 2023 in cs.CR

Abstract: The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of theses properties.

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Security and Privacy on Generative Data in AIGC: A Comprehensive Examination

The paper "Security and Privacy on Generative Data in AIGC: A Survey" by Wang et al. provides a detailed exploration of the security and privacy issues within the domain of Artificial Intelligence-Generated Content (AIGC). As the capabilities of generative models like GANs and DMs reach new heights, the paper underscores the necessity of scrutiny in handling generative data, especially considering privacy leakages, media forgeries, and other security vulnerabilities.

Overview and Classification of Issues

The paper systematically categorizes issues associated with generative data under four core properties of information security: privacy, controllability, authenticity, and compliance.

  1. Privacy: The paper explores two perspectives:
    • Privacy in AIGC: Where generative data potentially mirrors sensitive content from its training data.
    • AIGC for Privacy: Capitalizing on generative data to replace or obscure sensitive information in real data.
  2. Controllability: It stresses the importance of controlling access to generative data, highlighting:
    • Access Control: Techniques like adversarial perturbations prevent unauthorized manipulations.
    • Traceability: Methods such as digital watermarking and blockchain for tracking the origin and use of generative data.
  3. Authenticity: The challenge is distinguishing between real and generative data, with emphasis on:
    • Generative Detection: Identifying generative data using its inherent artifacts or traces.
    • Generative Attribution: Tracing generative outputs back to their respective models.
  4. Compliance: It examines the regulatory landscape ensuring generative data adheres to standards of:
    • Non-toxicity: Preventing the generation of harmful content.
    • Factuality: Ensuring that the generated content is accurate and not misleading.

Numerical Results and Countermeasures

The paper provides a substantive foundation by presenting diverse methods and empirical results for tackling these identified threats. For example, differential privacy methods are shown to offer respectable utility while ensuring data protection, though challenges remain in maintaining fidelity across tasks. Techniques such as Stable Signature and DiffusionShield underscore the advancement in watermarking methods, improving traceability despite potential robustness challenges. Additionally, the emergent drive towards provable privacy through differential privacy and adversarial perturbations is particularly noteworthy, albeit complex in balancing with utility.

Implications and Future Directions in AIGC

The research highlights the pressing need for foundational advancements to effectively bridge the gap between theoretical frameworks and practical implementations. As models scale, the balancing act between mitigating privacy risks and maintaining data utility becomes increasingly critical. Future efforts should emphasize enhancing the robustness and scalability of adversarial perturbation and watermarking techniques across diverse generative architectures. Moreover, addressing the nuanced challenges posed by compliance, particularly in terms of bias and fairness, remains an urgent area for further exploration.

The survey by Wang et al. provides a thorough expertise-based roadmap for understanding and addressing the cybersecurity and privacy nuances in AIGC. As AI technologies continue to evolve and permeate our digital infrastructure, such comprehensive analyses are indispensable in guiding both academic inquiry and industry practices towards more secure and privacy-preserving generative systems.

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Authors (6)
  1. Tao Wang (700 papers)
  2. Yushu Zhang (43 papers)
  3. Shuren Qi (10 papers)
  4. Ruoyu Zhao (12 papers)
  5. Zhihua Xia (21 papers)
  6. Jian Weng (50 papers)
Citations (29)
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