Abstract
In this paper, we propose a method to increase the robustness of 2D/3D optical image encryption using the dilated deep convolutional neural network (CNN). In order to solve the problem that encrypted images suffer from some attacks in practical application, we utilize a fast and effective CNN denoiser based on the principle of deep learning. The CNN improves the robustness of the algorithm by improving the resolution of the reconstructed images. Besides, CNN has a high performance against blur and occlusion attacks. We introduce the pixel scrambling method to enhance the security level of the encryption by the private key of pixel scrambling operation. The proposed method can not only realize the encryption of a two-dimensional image but also implement three-dimensional image encryption by combining the integral imaging technology. Double random phase encoding in the fractional Fourier domain is selected for experimental verification, and the results show the capability for robustness, noise immunity, and security of the proposed method.
Highlights
With the rapid development of multimedia technology, it is of considerable significance to take efficient and high-security measures to protect the information at the same time
It is divided into 16384-pixel blocks with the size of 2 × 2 pixels, the scrambled image is obtained after pixel scrambling process, and present as noise distribution, the input image content cannot be recognized directly from them, as shown in Figs. 4(b) and (f)
In this paper, a deep learning method to improve the robustness of 2D/3D image encryption is proposed
Summary
With the rapid development of multimedia technology, it is of considerable significance to take efficient and high-security measures to protect the information at the same time. The key contributions of the proposed method are summarized as follow: we introduce an effective CNN denoiser into the encryption to separate the noise from the noised decrypted image; the deep learning method can against noise attack and against blur and occlusion attacks; the CNN uses residual learning algorithm to enhance the accuracy and performance of the model; parametric rectified linear unit (ReLU) and batch normalization (BNorm) method are introduced to improve the speed and the performance of CNN; we use dilated filter to increase the size of receptive field while reducing the network depth as much as possible; the scrambling algorithm is added in the encryption process in order to further improve the security of the encryption system; the encryption system can realize 2D and 3D image
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