Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.Key Words: Biometrics, Cancelable Biometrics, Security, Biometric Salting, Non-Invertible Transformation INTRODUCTIONThe biometric traits possessed by each individual are unique and has the potential to recognize an individual. Biometric traits can be physical and behavioral. Therefore, biometrics are used for authentication or recognition of individuals for many critical applications like border control, access control, immigration, forensic and different law enforcement. There are two phases in every conventional biometric system: enrolment phase and authentication phase. In the enrolment stage, the original biometric trait is captured and saved in the database. During the authentication stage, the system matches that stored template every time the user access the system by providing the live biometric (Kaur et al. 2014).Compared to password or token based authentication system, biometric system using fingerprint, iris, face, voice, etc. provides better security as people cannot lose or forget their biometric trait. But, the advanced technology of today's world makes it possible to create a loophole in the biometric system. People leave their fingerprints on whatever they touch; hence one can easily steal the fingerprint and can even make an artificial finger using the stolen fingerprint. The person's face can be captured by the camera even from a distance without their concern. In such situation, the security of the biometric based authentication system is at a stake.To overcome the problem of the stolen biometrics, the researchers have developed the template protection schemes. The biometric template protection schemes are mainly divided into two categories: i) Biomet...

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Steganalysis is the art of detecting hidden messages embedded inside Steganographic Images. Steganalysis involves detection of steganography, estimation of message length and its extraction. Recently Steganalysis receives great deal of attention from the researchers due to the evolution of new, advanced and much secured steganographic methods for communicating secret information. This paper presents a universal steganalysis method for blocking recent steganographic techniques in spatial domain. The novel method analyses histograms of both the cover and suspicious image and based on the histogram difference it gives decision on the suspicious image of being stego or normal image. This method for steganalysis extracts a special pattern from the histogram difference of the cover and . By finding that specific pattern from the histogram difference of the suspicious and cover image it detects the presence of hidden message. The proposed steganalysis method has been experimented on a set of stego images where different steganographic techniques are used and it successfully detects all those stego images.

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INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease. OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest. METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest. RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%. CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.

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