Encrypted Image Feature Extraction by Privacy-Preserving MFS

Guoming Chen, Qiang Chen, Xiongyong Zhu, Yiqun Chen
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引用次数: 3

Abstract

Privacy preserve machine learning is a hot topic in multimedia domain. In this paper, we propose a secure multifractal feature extraction and representation method in the encrypted domain. We first use chaotic sequence to scramble the image in a block wise way, then according to the characteristic of chaotic sequence which preserves locally the randomness and maintain special periodicity we propose a multifractal feature extraction method in the encrypted domain. Experimental results showed that multifractal feature has a good distinguish ability in the encrypted domain.
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基于隐私保护的MFS加密图像特征提取
隐私保护机器学习是多媒体领域的研究热点。本文提出了一种安全的加密域多重分形特征提取与表示方法。首先利用混沌序列对图像进行分块置乱,然后根据混沌序列局部保持随机性和保持特殊周期性的特点,提出了一种加密域多重分形特征提取方法。实验结果表明,多重分形特征在加密域具有良好的识别能力。
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