Compressive Sensing Based Audio Scrambling Using Arnold Transform
In this paper, a novel idea for scrambling the compressive sensed audio data using two dimensional Arnold transform is presented. In the proposed method, Arnold matrix is constructed by the numbers generated by using a secret key and a logistic map. A key
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Abstract. In this paper, a novel idea for scrambling the compressive sensed audio data using two dimensional Arnold transform is presented. In the proposed method, Arnold matrix is constructed by the numbers generated by using a secret key and a logistic map. A key based measurement matrix is used for compressive sensing to avoid the transmission and storage requirement of the matrix and to improve the security. The combination of compressive sensing and arnold scrambling provides very high security and ensures efficient channel usage, resistivity to noise, best signal to noise ratio and good scrambling of data. Experimental results confirm the effectiveness of the proposed scheme.
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Introduction
Scrambling is a technique which is mainly used in data hiding, watermarking and encryption applications for providing information security against illegal surveillance and wire tapping. In the time domain scrambling process, a segment of time domain sample values are taken and scrambles them into a different segment of samples. At the receiving end, the scramled data is descrambled into its original form. Both the scrambling and descrambling operations are based on a scrambling matrix. The disadvantage of audio scrambling matrices constructed by pseudorandom sequences [6], Hadamard transform [10] and Fibonacci transform [8] is that, since these matrices are invariable, they could easily be deciphered. Some improved algorithms such as stochastic matrix [5] and latin square [9] were developed to overcome this problem, but they result in heavy transmission load. Speech compression methods like G.729 mixed excitation linear prediction (MELP) and adaptive multi-rate (AMR) [11] audio codec are then employed along with the process of scrambling to reduce the transmission load, but these methods shows low robustness in the presence of noise. The degree of security of a scrambling algorithm depends on residual intelligibility and key space [2]. Residual intelligibility is the amount of intelligibility left over in the scrambled signal. The lower the residual intelligibility of a scrambling method, the higher its degree of security. Scrambling degree (SD) [7] can be used to evaluate the degree of security. As SD increases, degree of security increases and residual intelligibility decreases. Key space is the number of keys available for scrambling. Larger the key space better will be the degree of security. G. Mart´ınez P´ erez et al. (Eds.): SNDS 2014, CCIS 420, pp. 172–183, 2014. c Springer-Verlag Berlin Heidelberg 2014
Compressive Sensing Based Audio Scrambling Using Arnold Transform
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An efficient scrambling method should be channel-saving, attack-resistant and should provide high scrambling degree. Since compressive sensing (CS) [3] provides very good compression and robustness whereas Arnold scrambling [12] provides very good scrambling degree, by combining both these techniques, an effective audio scrambling method can be developed. In the proposed scheme, compressive sensing is applied on the audio signal and the resultant
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