The Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center for Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
Sensors (Basel). 2024 Mar 26;24(7):2128. doi: 10.3390/s24072128.
In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis-dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption.
本研究提出了一种基于光学相干断层扫描（OCT）结合卷积神经网络（CNN）的 U-Net 结构的内部指纹引导指尖皮肤表皮厚度的光学图像加密方法。通过对横截面 OCT 图像中表皮层上下边界之间的距离进行计算，得出指尖皮肤的表皮厚度，并使用 CNN 对其进行分割，然后基于最大强度投影（MIP）算法提取表皮-真皮交界处（DEJ）的内部指纹。实验结果表明，由于正常相关系数和不同压力下指尖皮肤表皮厚度图之间的加密过程，内部指纹引导的表皮厚度对压力不敏感。此外，数值模拟结果通过原始图像和使用正确和错误密钥解密后的恢复图像之间的结构相似性指数矩阵（SSIM）分析，分别证明了加密方案的可行性和安全性。基于 SSIM 值，从三个方面对稳健性进行了分析：不同压力、噪声攻击和数据丢失。通过灰度直方图验证了密钥的随机性，计算了三个方向上相邻像素值的平均相关系数和平均熵。本研究表明，指尖皮肤的表皮厚度可以作为信息加密的重要生物特征信息。
