A new approach of object recognition in encrypted images using convolutional neural network
Zina tayari, nasreddine hnana, Mourad Zaied · 2020
One of the major challenges in mobile networks and digital technologies is maintaining the security of real time data. In this regard, the research community developed a lot of works to fulfill this goal by proposing secure image encryption algorithms. However, some of these encryption schemes are not secure enough and lack robustness and security. In this paper, we succeed to reveal the weaknesses of a recently published encryption algorithm that is supposed to be secure and robust. We found that although the proposed network is unable to decrypt the ciphered image, it is able to perform classification on this image. We succeeded to build a deep neural network that can recognize encrypted images with an accuracy of 95.8%. Results demonstrate that our proposed approach is efficient for classifying ciphered images. These results could be valuable for further works into the topic of cryptanalysis using deep learning.