Interval type-2 fuzzy logic system based similarity evaluation for image steganography

Zubair Ashraf; Mukul Lata Roy; Pranab K Muhuri; Q M Danish Lohani

doi:10.1016/j.heliyon.2020.e03771

Interval type-2 fuzzy logic system based similarity evaluation for image steganography

Heliyon. 2020 May 7;6(5):e03771. doi: 10.1016/j.heliyon.2020.e03771. eCollection 2020 May.

Authors

Zubair Ashraf¹, Mukul Lata Roy¹, Pranab K Muhuri¹, Q M Danish Lohani²

Affiliations

¹ Department of Computer Science, South Asian University, Akbar Bhavan, Chanakyapuri, New Delhi 110021, India.
² Department of Mathematics, South Asian University, Akbar Bhavan, Chanakyapuri, New Delhi 110021, India.

Abstract

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. Thus, this paper proposes an interval type-2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as 'IT2 FLS-LSB method'. Moreover, we have developed two more methods, namely, type-1 fuzzy logic system based least significant bits (T1FLS-LSB) and Euclidean distance based similarity measures for least significant bit (SM-LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR (peak signal-to-noise ratio), UQI (universal quality index), and SSIM (structural similarity measure) are used. All the three steganographic methods are applied on dataset and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS-LSB method. We have also demonstrated the high payload capacity of our proposed method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

Keywords: Computer science; Data hiding; Image steganography; Interval type-2 fuzzy logic system; Similarity measure.