SecureEI:边缘智能人工智能模型的主动知识产权保护

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Peihao Li , Jie Huang , Shuaishuai Zhang , Chunyang Qi
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引用次数: 0

摘要

在边缘计算平台上部署人工智能模型可以提高实时性能、降低网络依赖性并确保终端设备上的数据隐私。然而,与基于云的解决方案相比,边缘环境容易受到物理和网络攻击,因此模型泄漏和滥用的风险也随之增加。为了降低这些风险,我们提出了 SecureEI,这是一种利用模型分割和数据中毒技术为人工智能模型提供主动知识产权保护的方法。SecureEI 将模型分为两个部分:DeviceNet(将输入数据处理成受保护的许可数据)和 EdgeNet(对许可数据进行操作以执行预定任务)。这种方法确保只有经过转换的许可证数据才能产生高模型准确性,而原始数据即使在微调攻击下也无法识别。我们进一步采用有针对性的训练策略和权重调整,以增强模型对潜在攻击的抵抗力,从而恢复其对原始数据的识别能力。在 MNIST、Cifar10 和 FaceScrub 数据集上进行的评估表明,SecureEI 不仅能在许可数据上保持较高的模型准确性,还能显著增强对微调攻击的防御能力,在保护边缘平台上的人工智能知识产权方面优于现有的最先进技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SecureEI: Proactive intellectual property protection of AI models for edge intelligence
Deploying AI models on edge computing platforms enhances real-time performance, reduces network dependency, and ensures data privacy on terminal devices. However, these advantages come with increased risks of model leakage and misuse due to the vulnerability of edge environments to physical and cyber attacks compared to cloud-based solutions. To mitigate these risks, we propose SecureEI, a proactive intellectual property protection method for AI models that leverages model splitting and data poisoning techniques. SecureEI divides the model into two components: DeviceNet, which processes input data into protected license data, and EdgeNet, which operates on the license data to perform the intended tasks. This method ensures that only the transformed license data yields high model accuracy, while original data remains unrecognizable, even under fine-tuning attacks. We further employ targeted training strategies and weight adjustments to enhance the model’s resistance to potential attacks that aim to restore its recognition capabilities for original data. Evaluations on MNIST, Cifar10, and FaceScrub datasets demonstrate that SecureEI not only maintains high model accuracy on license data but also significantly bolsters defense against fine-tuning attacks, outperforming existing state-of-the-art techniques in safeguarding AI intellectual property on edge platforms.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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