A New Image Steganographic Technique using Pattern based Bits Shuffling and Magic LSB for Grayscale Images

1. INTRODUCTION Steganography is the art and science of covert communication where secret data is hidden in innocent carriers, making it undetectable by human visual system (HVS). To increase the security of steganography, the secret information is encrypted using a cryptographic algorithm before message concealment. Steganography can be used for a number of useful applications including national and international governments' level security and secure banking and voting systems. In negative sense, it can be used by terrorists and criminals for their secret communication A number of different factors are considered while designing a new steganographic technique including payload, imperceptibility, and robustness. Payload is the amount of secret data that can be hidden in a particular carrier, measured in bits per pixel (bpp). Imperceptibility shows the chances of undetectability by HVS, which is measured using different image quality assessment metrics (IQAMs) like peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), and structural similarity index metric (SSIM) Ji and Baik 2015). Robustness is the level of difficulty faced by attackers while extracting the hidden data from the stego image. The more security levels are introduced in a particular steganographic algorithm, the more robust is the steganography algorithm The simplest spatial domain method of data hiding is LSB substitution, where the LSBs of the host image are replaced with the bits of secret data. Although the LSB method is quite simple and it results in good quality stego images but its detection is very easy. To decrease the asymmetric artefacts in stego images and make the detection of secret data difficult, LSB matching (LSB-M) is proposed(Luo, Huang and Huang 2010). LSB and LSB-M interpret the image pixels independently during data hiding which produces obvious artefacts in stego images. To solve this issue, (Mielikainen 2006) proposed LSB matching revisited (LSB-MR) which hides two secret bits at a time and takes into consideration the relationship between two neighboring pixels. LSB, LSB-M, and LSB-MR hide data directly in cover images without encryption. Furthermore, the image pixels are interpreted using raster scanning (starting from left-top and proceeding towards right-bottom). These two operations make the extraction of secret data easy for attackers. In addition, some of the existing techniques produce stego images of low quality, whose detection is relatively easy by HVS. To address these limitations, this paper proposes a spatial domain based steganographic technique for grayscale images using M-LSB and PBSA. The main contributions of this paper …