28-07-2012, 04:18 PM
Design and VLSI Implementation of Modified AES with Neural Networks for Image Coding
[attachment=29422]
ABSTRACT:
The need to transmit data over internet or any other network is increasing at a very fast pace, which requires techniques that can considerably reduce the size of images so that they occupy less space and bandwidth for transmission, for this a direct solution method is used for image compression using the neural network. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access, by using Advanced Encryption Standard (AES) a cryptographic algorithm; this can protect electronic data and gives better security to the image by its encryption algorithm.
Introduction
The present scenario is that, the usage of autonomous vehicles such as UAV (unmanned aerial vehicle), Robots or unmanned military vehicles are growing especially in applications such as manufacturing, hazardous materials handling, surveillance, etc. The basic task in any such application is the perception of the environment through one or more sensors. Processing of the sensor input results in a particular representation of the unknown environment, which can then be used for navigating and controlling the vehicle. The general sensors used for autonomous vehicles include infra-red, sonar, laser, radar and so on [1].
Image Compression
Image compression research aims at reducing the number of bits needed to represent an image. Image compression algorithms take into account the psycho visual features both in space and frequency domain and exploit the spatial correlation along with the statistical redundancy. However, usages of the algorithms are dependent mostly on the information contained in images. A practical compression algorithm for image data should preserve most of the characteristics of the data while working in a lossy manner and maximize the gain and be of lesser algorithmic complexity. In general almost all the traditional approaches adopt a two-stage process; first, the data is transformed into some other domain and or represented.
Encryption through AES
Encryption is a common technique to uphold image security. Image and video encryption have applications in various fields including internet communication, multimedia systems, medical imaging, Tele-medicine and military communication. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. In October 2000, NIST (National Institute of Standards and Technology) selected Rijndael as the new Advanced Encryption Standard (AES), in order to replace the old Data Encryption Standard (DES) and triple DES.
CONCLUSION
Investigations into the application of neural networks to the problem of image compression have produced some promising results. By their very nature, neural networks are well suited to the task of processing image data. The characteristics of artificial neural networks which include a massively parallel structure, a high degree of interconnection, the propensity for storing experiential knowledge, and the ability to self-organize, parallel many of the characteristics of own visual system. A new modified version of AES, to design a secure symmetric image encryption technique, has been proposed. The AES is extended to support a key stream generator for image encryption which can overcome the problem of textured zones existing in other known encryption algorithms.
[attachment=29422]
ABSTRACT:
The need to transmit data over internet or any other network is increasing at a very fast pace, which requires techniques that can considerably reduce the size of images so that they occupy less space and bandwidth for transmission, for this a direct solution method is used for image compression using the neural network. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access, by using Advanced Encryption Standard (AES) a cryptographic algorithm; this can protect electronic data and gives better security to the image by its encryption algorithm.
Introduction
The present scenario is that, the usage of autonomous vehicles such as UAV (unmanned aerial vehicle), Robots or unmanned military vehicles are growing especially in applications such as manufacturing, hazardous materials handling, surveillance, etc. The basic task in any such application is the perception of the environment through one or more sensors. Processing of the sensor input results in a particular representation of the unknown environment, which can then be used for navigating and controlling the vehicle. The general sensors used for autonomous vehicles include infra-red, sonar, laser, radar and so on [1].
Image Compression
Image compression research aims at reducing the number of bits needed to represent an image. Image compression algorithms take into account the psycho visual features both in space and frequency domain and exploit the spatial correlation along with the statistical redundancy. However, usages of the algorithms are dependent mostly on the information contained in images. A practical compression algorithm for image data should preserve most of the characteristics of the data while working in a lossy manner and maximize the gain and be of lesser algorithmic complexity. In general almost all the traditional approaches adopt a two-stage process; first, the data is transformed into some other domain and or represented.
Encryption through AES
Encryption is a common technique to uphold image security. Image and video encryption have applications in various fields including internet communication, multimedia systems, medical imaging, Tele-medicine and military communication. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. In October 2000, NIST (National Institute of Standards and Technology) selected Rijndael as the new Advanced Encryption Standard (AES), in order to replace the old Data Encryption Standard (DES) and triple DES.
CONCLUSION
Investigations into the application of neural networks to the problem of image compression have produced some promising results. By their very nature, neural networks are well suited to the task of processing image data. The characteristics of artificial neural networks which include a massively parallel structure, a high degree of interconnection, the propensity for storing experiential knowledge, and the ability to self-organize, parallel many of the characteristics of own visual system. A new modified version of AES, to design a secure symmetric image encryption technique, has been proposed. The AES is extended to support a key stream generator for image encryption which can overcome the problem of textured zones existing in other known encryption algorithms.