A Covert Encryption Method for Applications in Electronic Data Interchange

A principal weakness of all encryption systems is that the output data can be ‘seen’ to be encrypted. In other words, encrypted data provides a ‘flag’ on the potential value of the information that has been encrypted. In this paper, we provide a new approach to ‘hiding’ encrypted data in a digital image. In conventional (symmetric) encryption, the plaintext is usually represented as a binary stream and encrypted using an XOR type operation with a binary cipher. The algorithm used is ideally designed to: (i) generate a maximum entropy cipher so that there is no bias with regard to any bit; (ii) maximize diffusion in terms of key dependency so that a change in any bit of the key can effect any, and potentially all, bits of the cipher. In the work reported here, we consider an approach in which a binary or low-bit plaintext image is encrypted with a decimal integer or floating point cipher using a convolution operation and the output quantized into a 1-bit array generating a binary image ciphertext. This output is then ‘embedded’ in a host image to hide the encrypted information. Embedding is undertaken either in the lowest 1-bit layer or multiple 1-bit layers. Decryption is accomplished by: (i) extracting the binary image from the host image; (ii) correlating the result with the original cipher. In principle, any cipher generator can be used for this purpose and the method has been designed to operate with 24-bit colour images. The approach has a variety of applications and, in this paper, we focus on the authentication and self-authentication of e-documents (letters and certificates, for example) that are communicated over the Internet and are thereby vulnerable to attack (e.g. modification, editing, counterfeiting etc.). In addition to document authentication, the approach considered provides a way of propagating disinformation and a solution to scenarios that require ‘plausible deniability’

An Optical Machine Vision System for Applications in Cytopathology

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a novel technique for the creation and extraction of information from a membership function considered. The methods discussed and the algorithms developed have a range of applications and in this work, we focus the engineering of a system for automating a Papanicolaou screening test

A Covert Encryption Method for Applications in Electronic Data Interchange

A principal weakness of all encryption systems is that the output data can be ‘seen’ to be encrypted. In other words, encrypted data provides a ‘flag’ on the potential value of the information that has been encrypted. In this paper, we provide a new approach to ‘hiding’ encrypted data in a digital image. In conventional (symmetric) encryption, the plaintext is usually represented as a binary stream and encrypted using an XOR type operation with a binary cipher. The algorithm used is ideally designed to: (i) generate a maximum entropy cipher so that there is no bias with regard to any bit; (ii) maximize diffusion in terms of key dependency so that a change in any bit of the key can effect any, and potentially all, bits of the cipher. In the work reported here, we consider an approach in which a binary or low-bit plaintext image is encrypted with a decimal integer or floating point cipher using a convolution operation and the output quantized into a 1-bit array generating a binary image ciphertext. This output is then ‘embedded’ in a host image to hide the encrypted information. Embedding is undertaken either in the lowest 1-bit layer or multiple 1-bit layers. Decryption is accomplished by: (i) extracting the binary image from the host image; (ii) correlating the result with the original cipher. In principle, any cipher generator can be used for this purpose and the method has been designed to operate with 24-bit colour images. The approach has a variety of applications and, in this paper, we focus on the authentication and self-authentication of e-documents (letters and certificates, for example) that are communicated over the Internet and are thereby vulnerable to attack (e.g. modification, editing, counterfeiting etc.). In addition to document authentication, the approach considered provides a way of propagating disinformation and a solution to scenarios that require ‘plausible deniability’

A Surface Inspection Machine Vision System that Includes Fractal Texture Analysis

The detection, recognition and classification of features in a digital image is an important component of quality control systems in production and process engineering and industrial systems monitoring, in general. In this paper, a new pattern recognition system is presented that has been designed for the specific task of monitoring the quality of sheet-steel production in a rolling mill. The system is based on using both the Euclidean and Fractal geometric properties of an imaged object to develop training data that is used in conjunction with a supervised learning procedure based on the application of a fuzzy inference engine. Thus, the classification method includes the application of a set of features which include fractal parameters such as the Lacunarity and Fractal Dimension and thereby incorporates the characterisation of an object in terms of texture that, in this application, has metallurgical significance. The principal issues associated with object recognition are presented including a new segmentation algorithm. The selflearning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a new technique for the creation and extraction of information from a membership function considered. The methods discussed, and the system developed, have a range of applications in ‘machine vision’ and automatic inspection. However, in this publication, we focus on the development and implementation of a surface inspection system designed specifically for monitoring surface quality in the manufacture of sheet-steel. For this publication, we include a demonstration version of the system which can be downloaded, installed and utilised by interested readers as discussed in Section VI

ReRoROS: Recycled Robot Operating System

This is a basic operating system for a recycled robot based on the Pioneer series of robots e.g. Pioneer 3 and Peoplebot. The original onboard computer from the Pioneer machines were replaced with Jetson Nano's. These were connected to the Hitachi driver processors using a serial connection via the Jetson's USB. The operating system was wrote in Python as an expandable, easily modified system for basic motion and server reporting