JPEG2000 compatible neural network based cipher

In this paper, an efficient encryption technique is proposed, especially for JPEG2000 compatible images.The technique uses a multilayer neural network to generate a pseudo-random sequence for transforming wavelet subbands into cipher subbands.The neural network generator takes 64 bit key as a startup seed with additional 64 bit key for initial weights and biases.At each layer, output is calculated by several iterations to increase the complexity of the pseudorandom sequence generation.In order to examine effectiveness of this approach, various tests including correlation, histogram, key space etc. are conducted on test images, and the results demonstrate the robustness of the proposed approach

Embedding Authentication and DistortionConcealment in Images – A Noisy Channel Perspective

In multimedia communication, compression of data is essential to improve transmission rate, and minimize storage space. At the same time, authentication of transmitted data is equally important to justify all these activities. The drawback of compression is that the compressed data are vulnerable to channel noise. In this paper, error concealment methodologies with ability of error detection and concealment are investigated for integration with image authentication in JPEG2000.The image authentication includes digital signature extraction and its diffusion as a watermark. To tackle noise, the error concealment technologies are modified to include edge information of the original image.This edge_image is transmitted along with JPEG2000 compressed image to determine corrupted coefficients and regions. The simulation results are conducted on test images for different values of bit error rate to judge confidence in noise reduction within the received images

Approximate trigonometric expansions with applications to signal decomposition and coding

Signal representation and data coding for multi-dimensional signals have recently received considerable attention due to their importance to several modern technologies. Many useful contributions have been reported that employ wavelets and transform methods. For signal representation, it is always desired that a signal be represented using minimum number of parameters. The transform efficiency and ease of its implementation are to a large extent mutually incompatible. If a stationary process is not periodic, then the coefficients of its Fourier expansion are not uncorrelated. With the exception of periodic signals the expansion of such a process as a superposition of exponentials, particularly in the study of linear systems, needs no elaboration. In this research, stationary and non-periodic signals are represented using approximate trigonometric expansions. These expansions have a user-defined parameter which can be used for making the transformation a signal decomposition tool. It is shown that fast implementation of these expansions is possible using wavelets. These approximate trigonometric expansions are applied to multidimensional signals in a constrained environment where dominant coefficients of the expansion are retained and insignificant ones are set to zero. The signal is then reconstructed using these limited set of coefficients, thus leading to compression. Sample results for representing multidimensional signals are given to illustrate the efficiency of the proposed method. It is verified that for a given number of coefficients, the proposed technique yields higher signal to noise ratio than conventional techniques employing the discrete cosine transform technique

Transform coding of signals using approximate trigonometric expansions

For signal representation it is always preferred that a signal be represented using a minimum number of parameters. in any transform coding scheme, the central operation is the reduction of correlation and thereby with appropriate coding of the transform coefficients, allows data compression to be achieved. The objective of data encoding is to transform a data array into a statistically uncorrelated set. This step is typically considered a decorrelation step, because in the case of unitary transformations the resulting transform coefficients are relatively uncorrelated. Most unitary transforms have the tendency to compact the signal energy into relatively few coefficients. The compaction of energy thus achieved permits a prioritization of the spectral coefficients, with the most energetic ones receiving a greater allocation of encoding bits. The transform efficiency and ease of implementation are to a large extent mutually incompatible. There are various transforms such as Karhunen-Loeve, discrete cosine transforms etc., but the choice depends on the amount of reconstruction error that can be tolerated and the computational resources available. We apply an approximate Fourier series expansion (AFE) to sampled one-dimensional signals and images, and investigate some mathematical properties. Additionally we extend the expansion to an approximate cosine expansion (ACE) and show that, for the purpose of data compression with minimum error reconstruction of images, the performance of ACE is better than AFE. For comparison purposes, the results are also compared with a discrete cosine transform (DCT)

Multi-Layered Multimodal Biometric Authentication for Smartphone Devices

As technological advances in smartphone domain increase, so are the issues that pertain to security and privacy. In current literature, multimodal biometric approach is addressed at length for purpose of improving secured access into personal devices. Moreover, most of the financial institutions such as banks, etc. enforce two or three step access into their corporate data to enforce security. However, personal devices currently do not support similar applications or way of enforcing multilayered access to its different domains/regions of data. In this paper, a multilayered multimodal biometric approach using three biometric methods (such as finger print, face and voice) is proposed for smartphones. It is shown that fusion of biometric methods can be layered to enforce secured access to private data on smartphone. The experimental results are presented

Intelligent Network System for Process Control: Applications, Challenges, Approaches

Non