  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. 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,
UQI, and SSIM are used. All the three steganographic methods are applied on
datasets 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. 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.
