Localization and Correction of Corrupted Pixel Blocks in Noisy Encrypted Images

Abstract

Digital data such as images must be secured during transmission or cloud storage. Image encryption algorithms can be a solution to this problem, but these approaches are very noise sensitive. Because of the introduction of noise, the original image cannot be recovered, even if we know the secret key. In this paper, we propose a new noisy encrypted image correction algorithm containing a convolutional neural network (CNN) training stage and then, two main steps. After a direct decryption of a noisy encrypted image, the first step is to identify and localize the blocks that are probably incorrectly decrypted using a fine-tuned CNN. The second step of our proposed approach is to analyze and correct these blocks. Experimental results show that the proposed method can be used to blindly correct noisy encrypted images, while preserving the image structure and without increasing the original data size with additional information, unlike error correcting codes.Index Terms-Multimedia security, image encryption, image denoising, deep learning, convolutional neural network, signal processing in the encrypted domain.