Distinguishing Lightweight Block Ciphers in Encrypted Images

Modern day lightweight block ciphers provide powerful encryption methods for securing IoT communication data. Tiny digital devices exchange private data which the individual users might not be willing to get disclosed. On the other hand, the adversaries try their level best to capture this private data. The first step towards this is to identify the encryption scheme. This work is an effort to construct a distinguisher to identify the cipher used in encrypting the traffic data. We try to establish a deep learning based method to identify the encryption scheme used from a set of three lightweight block ciphers viz. LBlock, PRESENT and SPECK. We make use of images from MNIST and fashion MNIST data sets for establishing the cryptographic distinguisher. Our results show that the overall classification accuracy depends firstly on the type of key used in encryption and secondly on how frequently the pixel values change in original input image

Review of factors affecting facial recognition algorithms performance

The face is a significant part of the human body, recognizing people in large groups of individuals. Thus, because of its uniqueness and universality, it has turned into the most generally utilized and acknowledged biometric technique. Many algorithms have been used by various researchers for face detection and recognition. Research, innovation progression, and applications consolidating face recognition in the last twenty years have grown massively. In this paper, some essential existing approaches which are adjusted with dealing with the issues of face recognition have been presented close by their Face recognition accuracy and the variables capable of debasing the performance of the review. In the first section, various factors that decrease facial detection and recognition accuracy have been researched like posture variety, illumination, aging, facial expressions, etc. While in the second section of the paper, various methods have been examined that attempt to relieve the impact of discussed factors. Various algorithms give various exhibitions in various conditions like enlightenment, noise, posture, and mask change. All the previously mentioned methods are represented briefly to give an overall idea. The motive of the paper is to carry all the various methods to a similar spot and simplify it to review the paper

Review of Factors Affecting Facial Recognition Algorithms Performance

The face is a significant part of the human body, recognizing people in large groups of individuals. Thus, because of its uniqueness and universality, it has turned into the most generally utilized and acknowledged biometric technique. Many algorithms have been used by various researchers for face detection and recognition. Research, innovation progression, and applications consolidating face recognition in the last twenty years have grown massively. In this paper, some essential existing approaches which are adjusted with dealing with the issues of face recognition have been presented close by their Face recognition accuracy and the variables capable of debasing the performance of the review. In the first section, various factors that decrease facial detection and recognition accuracy have been researched like posture variety, illumination, aging, facial expressions, etc. While in the second section of the paper, various methods have been examined that attempt to relieve the impact of discussed factors. Various algorithms give various exhibitions in various conditions like enlightenment, noise, posture, and mask change. All the previously mentioned methods are represented briefly to give an overall idea. The motive of the paper is to carry all the various methods to a similar spot and simplify it to review the paper