An Improved Hashing Function for Human Authentication System: Near Set Approach

An Improved Hashing Function for Human Authentication System: Near Set Approach

Lamiaa M. El Bakrawy, Neveen I. Ghali
Copyright: © 2013 |Pages: 11
DOI: 10.4018/ijcvip.2013040103
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Biometrics have the great advantage of recognition based on an intrinsic aspect of a human being and thus requiring the person to be authenticated for physical presentation. Unfortunately, biometrics suffer from some inherent limitation such as high false rejection when the system works at a low false acceptation rate. In this paper, near set are implemented to improve the Standard Secure Hash Function SHA-1 (ISHA-1) for strict multi-modal biometric image authentication system. The proposed system is composed of five phases, starting from feature extraction and selection phase, hashing computing that uses the ISHA-1 phase, embedding watermark phase, extraction and decryption watermark phase, and finally the authentication phase. Experimental results showed that the proposed algorithm guarantees the security assurance and reduces the time of implementation.
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1. Introduction

Hash functions were introduced in cryptography to provide data integrity, message authentication, and digital signature (HouZhen, HuanGuo, QianHong, Yu, ChunLei, & Xin Yu, 2010; Tiwari & Asawa, 2010). A function that compresses an input of arbitrary large length into a fixed small size hash code is known as hash function (Andreeva & Preneel, 2009; Seoa, Haitsmab, Kalkerb, & Yooa, 2004; Song & JaJa, 2009). The input to a hash function is called a message or plain text and output is usually referred to as message digest or hash value. Hash function H is defined to be a transformation that takes an input x and returns a fixed size string, which is called the hash value h (that is, h = H(x)) (Tiwari, 2010).

For the cryptographic hash function following properties are essential. First to be preimage resistance, which means that for any given code h, it is computationally infeasible to find x such that H(x) = h. to be second preimage resistance, meaning that for any given input x, it is computationally infeasible to find y≠x with H(y) = H(x). Finally to be computationally infeasible to find any pair (x, y) such that H(y) = H(x), that is to be collision resistant. or an ideal hash function with an m-bit output, finding a preimage or a second preimage requires about 2 m operations and the fastest way to find a collision is a birthday attack which needs approximately 2m/2 operations (Tiwari, 2010; Tiwari & Asawa, 2010).

The SHA-1 is called secure because it is computationally infeasible to find a message which corresponds to a given message digest, or to find two different messages which produce the same message digest. Any change to a message in transit will, with very high probability, result in a different message digest, and the signature will fail to verify (Eastlake & Jones, 2001; Pongyupinpanich & Choomchuay, 2004).

Biometrics refer to the automated identification of a person depending on his/her physiological or behavioural characteristics. Biometric characteristics include fingerprint, face, DNA, ear, facial thermogram, hand thermogram, iris, retina, voice, etc. (Feng & Wah, 2002; Jain, Ross, & Prabakhar, 2004). Biometrics have been gradually increased with the current wide employment since the conventional knowledge-based technology which can be forgotten, like a password, or lost or stolen, like an personality card has a defect (Jain & Pankanti, 2000; Kim & Lee, 2009). Multi-modal biometrics refer to using multiple biometric indicators for identifying persons. It has been shown to raise accuracy (Jain, Bolle, & Pankanti, 1999) and population treatment, while decreasing vulnerability to spoofing (Snelick, Uludag, Mink1, Indovina1, & Jain, 2005; Vatsa, Singh, Noore, Houck, & Morris, 2006).

Strict image authentication methods do not allow any modification in the image data. These methods can be classified into two extensive categories depending on the techniques that are used: methods based on conventional cryptography and methods that use fragile watermarking (Haouzia & Nourneir, 2008). In this paper, near set is used for feature partitioning and selection. A new message digest algorithm ISHA-1 is similar to SHA-1 with deferent complexity of the address computations is proposed to optimize time delay with collision resistant and assures a good compression and preimage. ISHA-1 is applied to compress selected features to a fixed size. Finally, conventional cryptography and watermarking technique are employed to propose strict authentication technique for multimodal biometric image.

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