Li Xue, Zhang Tao, Liu Mingriu, Fu Ying, Zhong Haizheng
School of Physics and Electronic Engineering, Hebei Mizu Normal University, Chengde, 067000, China.
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Sciences & Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Adv Sci (Weinh). 2024 Oct;11(38):e2405667. doi: 10.1002/advs.202405667. Epub 2024 Aug 5.
The risk of information leaks increases as images become a crucial medium for information sharing. There is a great need to further develop the versatility of image encryption technology to protect confidential and sensitive information. Herein, using high spatial redundancy (strong correlation of neighboring pixels) of the image and the in situ encryption function of a quantum dot functionalized encryption camera, in situ image encryption is achieved by designing quantum dot films (size, color, and full width at half maximum) to modify the correlation and reduce spatial redundancy of the captured image during encryption processing. The correlation coefficients of simulated encrypted image closely apporach to 0. High-quality decrypted images are achieved with a PSNR of more than 35 dB by a convolutional neural network-based algorithm that meets the resolution requirements of human visual perception. Compared with the traditional image encryption algorithms, chaotic image encryption algorithms and neural network-based encryption algorithms described previously, it provides a universal, efficient and effective in situ image encryption method.
随着图像成为信息共享的关键媒介，信息泄露风险增加。迫切需要进一步拓展图像加密技术的通用性，以保护机密和敏感信息。在此，利用图像的高空间冗余性（相邻像素的强相关性）以及量子点功能化加密相机的原位加密功能，通过设计量子点薄膜（尺寸、颜色和半高宽）来改变相关性并在加密处理过程中降低捕获图像的空间冗余性，从而实现原位图像加密。模拟加密图像的相关系数接近0。通过基于卷积神经网络的算法实现了高质量解密图像，其峰值信噪比超过35 dB，满足人类视觉感知的分辨率要求。与传统图像加密算法、先前描述的混沌图像加密算法和基于神经网络的加密算法相比，它提供了一种通用、高效且有效的原位图像加密方法。
