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Received:April 15, 2024 Revised:May 14, 2024
Received:April 15, 2024 Revised:May 14, 2024
中文摘要: 模型混淆是指将神经网络等价地转换为另一种形式, 是一种高效且低成本的神经网络保护技术. 为了发现模型混淆的缺陷, 研究人员提出了模型反混淆技术, 以期望改进模型混淆方法. 然而, 现有的模型反混淆技术研究较少, 并且适用场景和反混淆效果有限. 因此, 本文提出一种基于神经机器翻译(neural machine translation, NMT)技术的模型反混淆方法. 该方法将模型的反混淆任务建模成一个seq2seq的任务, 首先对混淆模型进行更详细的序列表示, 然后对权重参数中的混淆信息进行识别并处理, 最后再使用基于NMT的模型进行反混淆翻译. 实验结果表明, 该方法弥补了已有方法的不足, 能够有效地捕捉模型的混淆特征并对模型的架构进行恢复, 可以作为一种模型反混淆的通用方案.
中文关键词: 神经网络模型混淆 神经网络模型反混淆 神经机器翻译 Transformer
Abstract:Model obfuscation refers to the equivalent transformation of neural networks into another form, which is an efficient and low-cost technique for protecting neural networks. To detect the flaws of model obfuscation, researchers have proposed model deobfuscation techniques in the hope of improving model obfuscation methods. However, model deobfuscation techniques are not fully explored, with limited applicability and effectiveness. Therefore, this study proposes a model deobfuscation method based on neural machine translation (NMT). This method models a deobfuscation task as a seq2seq task. It provides a more detailed sequential representation of the obfuscated model, identifies and processes the obfuscated information in the weight parameters, and utilizes an NMT-based model for deobfuscation translation. The experimental results demonstrate that this method addresses the shortcomings of existing methods, effectively capturing the obfuscation features and restoring the architectures of models. It can serve as a general solution to model deobfuscation.
keywords: neural network model obfuscation neural network model deobfuscation neural machine translation (NMT) Transformer
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朱浪,刘彬彬,李嘉璇,郑启龙.基于神经机器翻译的模型反混淆方法.计算机系统应用,2024,33(10):163-173
ZHU Lang,LIU Bin-Bin,LI Jia-Xuan,ZHENG Qi-Long.Model Deobfuscation Method Based on Neural Machine Translation.COMPUTER SYSTEMS APPLICATIONS,2024,33(10):163-173
朱浪,刘彬彬,李嘉璇,郑启龙.基于神经机器翻译的模型反混淆方法.计算机系统应用,2024,33(10):163-173
ZHU Lang,LIU Bin-Bin,LI Jia-Xuan,ZHENG Qi-Long.Model Deobfuscation Method Based on Neural Machine Translation.COMPUTER SYSTEMS APPLICATIONS,2024,33(10):163-173