Adaptive transfer learning-based cryptanalysis on double random phase encoding

Abstract

Encrypting data can convert original data into a form that cannot be recognized and conceal personal information. To ensure data security, analyzing cryptographic algorithms used in data encryption is critical. The previous works evaluated the robustness of the optical cryptographic algorithm, Double Random Phase Encoding (DRPE), and verified the vulnerability of DRPE to plaintext recovery attacks based on deep learning. Although they could prove that DRPE is not secure by recovering the DRPE images into the original images, they evaluated DRPE only on simple datasets, which were binary or grayscale images with a single object. Thus, in this paper, we investigated the previous works of plaintext recovery on DRPE by using complex datasets. To our knowledge, it is the first study to evaluate DRPE on complex datasets. In addition to the plaintext recovery, we classified DRPE images of complex datasets and verified the feasible points in DRPE for classification tasks based on deep learning. Adaptive transfer learning, the modified version of the classical transfer learning for DRPE image classification, was proposed and utilized to train the DRPE image classification models. Experimental results showed that recovering the DRPE images of the complex datasets was challenging. In contrast, the imperceptible features representing the class of the images were in DRPE images regardless of whether the dataset was simple or complex. The classification performance of the proposed DRPE image classification scheme was better than those of state-of-the-art classification schemes, even with a small dataset. Furthermore, data augmentation with the proposed scheme improved the classification accuracy in DRPE image classification on a small dataset. In addition, our DRPE image classification schemes were applied to encrypted images by other optical cryptographic algorithms and the classification results were compared with that of the encrypted images by classical DRPE. © 2023 Elsevier Ltd