In the light of recent security incidents, leading to compromise of services using single factor authentication mechanisms, industry and academia researchers are actively investigating novel multi-factor authentication schemes. Moreover, exposure of unprotected authentication data is a high risk threat for organizations with online presence. The challenge is: how to ensure security of multifactor authentication data without deteriorating the performance of an identity verification system? To solve this problem, we present a novel framework that applies random projections to biometric data (inherence factor), using secure keys derived from passwords (knowledge factor), to generate inherently secure, efficient and revocable/renewable biometric templates for users' verification. We evaluate the security strength of the framework against possible attacks by adversaries. We also undertake a case study of deploying the proposed framework in a two-factor authentication setup that uses users' passwords and dynamic handwritten signatures. Our system preserves the important biometric information even when the user specific password is compromised -a highly desirable feature but not existent in the state-of-the-art transformation techniques. We have evaluated the performance of the framework on three publicly available signatures datasets. The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results.

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III. (63,56) BCH ENCODEROn the encoder side, systematic encoding has been used, which makes easier implementation of encoder. It is shown in figure-Ion next page;The word 'ingenuity' can now be explained easily. The polynomial (2) is used for one-bit error correction specifically while considering n = 64 and k = 56. The other polynomial (3) is an even parity generator.(1)

Deepfake technology is an emerging technology prevailing in today's digital world. It is used to create fake videos by exploiting some of the artificial intelligence (AI) based techniques and deep learning methodology. The facial expressions and motion effects are primarily used to train and manipulate the seed frame of someone to generate the desired morphed video frames that mimic as if they are real. Deepfake technology is used to make a highly realistic fake video that can be widely used to spread the wrong information or fake news by regarding any celebrity or political leader which is not created by them. Due to the high impact of social media, these fake videos can reach millions of views within an hour and create a negative impact on our society. This chapter includes the crucial points on methodology, approach, and counter applications pertinent to deep-fake technology highlighting the issues, challenges, and counter measures to be adopted. Through observations and analysis, the chapter will conclude with profound findings and establishes the future directions of this technology.