Partha Pratim Sarkar

A novel technique for image steganography based on Block-DCT and Huffman Encoding

Image steganography is the art of hiding information into a cover image. This paper presents a novel technique for Image steganography based on Block-DCT, where DCT is used to transform original image (cover image) blocks from spatial domain to frequency domain. Firstly a gray level image of size M x N is divided into no joint 8 x 8 blocks and a two dimensional Discrete Cosine Transform (2-d DCT) is performed on each of the P = MN / 64 blocks. Then Huffman encoding is also performed on the secret messages/images before embedding and each bit of Huffman code of secret message/image is embedded in the frequency domain by altering the least significant bit of each of the DCT coefficients of cover image blocks. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message/image cannot be extracted without knowing decoding rules and Huffman table.

Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm.

A COMACT MICROSTRIP PATCH ANTENNA FOR WIRELESS COMMUNICATION

A single feed compact rectangular microstrip antenna is presented in this paper. A triangular slot is introduced at the upper edge of the patch to reduce the resonant frequency. A small piece of triangular patch is added within the area of the triangular slot to improve the gain bandwidth performance of the antenna. The antenna size has been reduced by 46.2% when compared to a conventional square microstrip patch antenna with a maximum of 160MHz bandwidth and i27:36dB return loss. The characteristics of the designed structure are investigated by using MoM based electromagnetic solver, IE3D. An extensive analysis of the return loss, radiation pattern, gain and e-ciency of the proposed antenna is shown in this paper. The simple conflguration and low proflle nature of the proposed antenna leads to easy fabrication and make it suitable for the

Image encryption using affine transform and XOR operation

Image encryption is a suitable method to protect image data. Image and text data has their unique features. The available encryption algorithms are good for text data. They may not be suitable for multimedia data. In fact the pixels of natural images are highly correlated to their neighboring pixels. Due to this strong correlation any pixel can be practically predicted from the values of its neighbors. In this article, we propose a new location transformation based encryption technique. We redistribute the pixel values to different location using affine transform technique with four 8-bit keys. The transformed image then divided into 2 pixels × 2 pixels blocks and each block is encrypted using XOR operation by four 8-bit keys. The total key size used in our algorithm is 64 bit which proves to be strong enough. The experimental results proved that after the affine transform the correlation between pixel values was significantly decreased.

Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training

Optimization algorithms are normally influenced by meta-heuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feed-forward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and it is incorporated into back-propagation algorithm to achieve fast and improved convergence rate in training feed-forward neural network. The proposed technique is tested over some standard data set. It is found that proposed method produces an improved convergence within very few iteration. This performance is also analyzed and compared to genetic algorithm based back-propagation. It is observed that proposed method consumes less time to converge and providing improved convergence rate with minimum feed-forward neural network design.

Audio Steganography Using GA

Audio Steganography is a method that ensures secured data transfer between parties normally in internet community. In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of Audio Steganography. We use most powerful encryption algorithm (RSA) to encrypt message in the first level of security, which is very complex to break. In the second level, we use a more powerful GA (Genetic Algorithm) based LSB (Least Significant Bit) Algorithm to encode the encrypted message into audio data. Here encrypted message bits are embedded into random and higher LSB layers, resulting in increased robustness against noise addition. On the other hand, GA operators are used to reduce the distortion.

A compact microstrip antenna for mobile communication

A single layer, single feed compact rectangular antenna is proposed. Resonant frequency has been reduced drastically by cutting unequal rectangular slots at the edge of the patch. Antenna size has been reduced by 46.13% with an increased frequency ratio.

A dual tuned Complementary Structure Frequency Selective surface for WLAN applications

A single substrate layer double metallic plane frequency selective surface (FSS) is designed with miniaturized electrical elements and two tunable pass-bands enabling Wireless Local Area Network applications. Each FSS periodic cell consists of a metallic square loop on the top metal layer and its complementary etched on the bottom metal layer separated by a thin dielectric layer. This designed FSS provides two tunable transmission bands. For predicting the frequency selective characteristics of the designed FSS an equivalent electric circuit model is also presented. To satisfy the needed requirements for Wireless Local Area Network (WLAN) applications the designed structure is optimized. Due to symmetrical nature of the design, the FSS is insensitive to variations of RF incidence angle for 90° (degree) rotations of the FSS. Simulated theoretical investigations are done by Ansoft Designer® software. Experimental investigation is performed using standard microwave test bench.

A Weighted Location Based LSB Image Steganography Technique

Steganography is the art of hiding the presence of communication by embedding secret messages into innocent, innocuous looking cover documents, such as digital images, videos, sound files. We present here a novel steganographic method based on affine cipher encryption algorithm and the least significant bit (LSB) substitution in order to provide a strong security and imperceptible visual quality to secret message. We encrypt the 8 bit secret image by changing pixel values using affine cipher. After that each 8 bit pixel of encrypted secret image is divided into 4 groups of 2 bit each. Each part which have a decimal value between 0 to 3 determines the location in each pixel of cover image where to embed the message. We do not store the actual secret message instead we encode the secret message into cover image using the value of each group of secret message. Since, we have two layers of encoding: one using private keys of affine cipher and other for steganography, our methods proves to be more secure than others. Our experimental results also proves that the proposed method has got an acceptable image quality as supported by PSNR values.

Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.