Earticle

Information hiding has importance in information security. Generally information hiding is used to protect, authenticate data or for secret communication such as military secret information. It can be achieved in communication protocols, digital multimedia content such as image, text and audio. Currently available information hiding techniques do not pay much attention on stego-object (hidden information in original object) with respect to its original cover (original) object. Both cover and stego-images are drifted in context of edge characteristics of image objects. That is not affordable in some application such as medical domains applications. This paper proposes an information hiding method around the edge boundary of objects in image. The stego-image can be utilized for further processing methods (such as segmentation, identification, and recognition of objects etc). The experimental results show that the stego-image has identical edge boundaries as is in cover-image (using ‘Sobel’ and ‘Canny’ edge detection methods), so stego-image can directly be used instead of cover-image for further image processing techniques. Proposed edge based information hiding scheme is secure due to its varying calculated thresholds and has less computational cost.

Fingerprint is a reliable biometric which is used for personal verification. Current fingerprint verification techniques can be broadly classified as Minutiae-based, ridge feature-based, correlation-based and gradient-based. In this paper, we propose use of the statistical texture analysis of a fingerprint using spatial grey level dependence method (SGLDM) for discrimination and personal verification. This method extracts texture features by an algorithm based on the spatial grey level dependence method. The fingerprint images were chosen from DB1 and DB2 fingerprint databases of FVC 2002. Results show that fingerprint texture feature can be reasonably used for discrimination and for personal verification.

Face recognition has been a very active research area in the past two decades. Many attempts have been made to understand the process how human beings recognize human faces. It is widely accepted that face recognition may depend on both componential information (such as eyes, mouth and nose) and non-componential/holistic information (the spatial relations between these features), though how these cues should be optimally integrated remains unclear. The present study, a different observer's view approach using eigen/fisher features of multi-scaled face components and Artificial Neural Network has been proposed. The basic idea of the proposed method is to construct facial feature vector by down sampling face components such as eyes, nose, mouth and whole face with different resolutions based on significance of face component, and then subspace Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) method is employed for further dimensionality reduction and good representation of facial features. Each face in data base to be recognized is projected on eigen space or fisher face to find its weight vector. The weight vector of face images to be trained become the input to neural network classifier, which uses Back Propagation/Radial basis function to recognize faces with variation in facial expression, and with / without spectacles. The proposed algorithm has been tested on 400 faces of 10 subjects of ORL data base and 500 faces of 100 subjects of FERET database results are encouraging compared to the existing methods in literature.

In this article, we describe the regression test process to test and verify the changes made on software. A developed technique use the automation test based on decision tree and test selection process in order to reduce the testing cost is given. The developed technique is applied to a practical case and the result show its improvement.

Voice-over-IP (VoIP) is being widely used to packetize and relay voice information over existing IP networks. Due to the cheaper costs involved in using VoIP as opposed to traditional PSTN networks it is quickly gaining momentum and has seen widespread use in both personal and business domains. If this technology is to grow and gain over normal phone calls it must fulfill certain conditions that Public Switched Telephone Networks (PSTN) currently provide. Since VoIP uses the same routes used by normal Internet traffic, it is prone to a wide range of security threats, similar to those faced by normal packet data. These include, but are not limited to, Denial of Service (DoS), intruders in the network who either eavesdrop or are able to make free calls, man-in-the-middle attacks, etc. It thus becomes imperative that a protocol be developed that is able to both guard against and take corrective action in the event of the occurrence of any attack. In this paper we propose a protocol that detects and prevents the occurrence of a distributed Denial of Service (DDoS) attack. It also enforces security mechanisms to ensure that an attacker has not gained access to a user's password and thus making illegitimate calls. We develop a trust mechanism that can be used to detect an attacker in the network. The protocol has been rigorously examined through a wide range of experiments and the results have been found to be quite promising.