剖析深度学习模型的生产安全风险并提出应对策略。
Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies
- 系统梳理当前部署模型的安全漏洞。
- 预测未来硬件与算法进步带来的新威胁。
- 提出可量化评估的防御方案，适合安全研究者参考。
随着深度学习模型在社会各领域的广泛部署，一系列独特挑战随之而来，主要集中在模型架构层面，这些风险对未来的成功应用构成重大挑战。本文系统分析了当前生产环境中深度学习模型面临的安全问题，并基于计算、人工智能及硬件技术的发展，前瞻性地预判未来可能涌现的安全挑战。此外，本文提出多种风险缓解策略，并设计可量化的评估指标，用于衡量各类防御措施的有效性，为深度学习系统的安全演进提供理论支持与实践指导。
原文摘要 · Abstract (English)
With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in the future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。