In the recent years, pixel-based perceptual algorithms have been successfully
applied for privacy-preserving deep learning (DL) based applications. However,
their security has been broken in subsequent works by demonstrating a
chosen-plaintext attack. In this paper, we propose an efficient pixel-based
perceptual encryption method. The method provides a necessary level of security
while preserving the intrinsic properties of the original image. Thereby, can
enable deep learning (DL) applications in the encryption domain. The method is
substitution based where pixel values are XORed with a sequence (as opposed to
a single value used in the existing methods) generated by a chaotic map. We
have used logistic maps for their low computational requirements. In addition,
to compensate for any inefficiency because of the logistic maps, we use a
second key to shuffle the sequence. We have compared the proposed method in
terms of encryption efficiency and classification accuracy of the DL models on
them. We have validated the proposed method with CIFAR datasets. The analysis
shows that when classification is performed on the cipher images, the model
preserves accuracy of the existing methods while provides better security.

本文提出了一种有效的基于像素的感知加密方法，采用混沌映射生成的序列对像素值进行异或运算。已验证该方法在保持原始图像固有属性的同时提供了必要的安全性，且应用于数据集 CIFAR 上时，其分类精度与现有方法相当，同时提高了安全性。