A Block-based Convolutional Neural Network for Low-Resolution Image Classification

The success of CNN-based architecture on image classification in learning and extracting features made them so popular these days, but the task of image classification becomes more challenging when we use state of art models to classify noisy and low-quality images. To solve this problem, we proposed a novel image classification architecture that learns subtle details in low-resolution images that are blurred and noisy. In order to build our new blocks, we used the idea of Res Connections and the Inception module ideas. Using the MNIST datasets, we have conducted extensive experiments that show that the introduced architecture is more accurate and faster than other state-of-the-art Convolutional neural networks. As a result of the special characteristics of our model, it can achieve a better result with fewer parameters.

PiouCrypt: Decentralized Lattice-based Method for Visual Symmetric Cryptography

In recent years, establishing secure visual communications has turned into one of the essential problems for security engineers and researchers. However, only limited novel solutions are provided for image encryption, and limiting the visual cryptography to only limited schemes can bring up negative consequences, especially with emerging quantum computational systems. This paper presents a novel algorithm for establishing secure private visual communication. The proposed method has a layered architecture with several cohesive components, and corresponded with an NP-hard problem, despite its symmetric structure. This two-step technique is not limited to gray-scale pictures, and furthermore, utilizing a lattice structure causes to proposed method has optimal resistance for the post-quantum era, and is relatively secure from the theoretical dimension.