The work, authored by Rupa Jamatia and Bubu Bhuyan of the Department of Information Technology, addresses one of the oldest tensions in the field. Steganography differs from encryption in a crucial way: cryptography scrambles a message so that intercepted text is unreadable, but the interception itself is obvious. Steganography instead hides the existence of the message. A secret payload is embedded inside an innocuous cover object, in this case a color image, so that anyone observing the traffic sees only an ordinary photograph. The price of that concealment is fragility. If the embedding process disturbs the cover image too much, statistical artifacts appear, and modern steganalysis tools can flag the image as suspicious. The art lies in embedding as much data as possible while leaving the smallest possible fingerprint.

The most straightforward way to hide bits in an image is least significant bit, or LSB, replacement. In a typical 24-bit color image, each pixel stores one byte each for its red, green and blue intensities. Flipping the lowest-order bit of a byte changes the intensity by at most one unit out of 255, a difference far below what the human eye can perceive. Sequential LSB embedding, however, has a well-known weakness: if the sender fills pixel after pixel in raster order, the hidden data occupies a predictable, contiguous region of the image. Steganalysis tools exploit exactly this regularity, scanning for zones where the statistical behavior of the least significant bits deviates from natural image noise. Randomizing where the bits land is therefore a central defense, and it is the pivot of the new study.

Jamatia and Bhuyan’s framework uses the XORShift64 generator, a pseudorandom number generator introduced by George Marsaglia in 2003, to decide which pixels receive secret bits. XORShift64 belongs to a family of generators prized for their extreme simplicity: the state is updated by a handful of bitwise exclusive-OR and shift operations, which modern processors execute in a few nanoseconds. The authors emphasize that the generator itself is a commonly used primitive; their contribution lies in how it is integrated into the embedding strategy. By seeding the generator and using its output to select pixel positions unpredictably, the scheme ensures that an adversary who does not know the seed cannot reconstruct the sequence of modified pixels, and the scattered, irregular pattern of changes minimizes the detectability of the resulting stego image. The computational overhead, they note, is minimal, which matters for practical deployment on constrained devices.

Capacity is the second pillar of the method. Rather than embedding a single bit per pixel, the XS-RPVI approach writes message bits into the least significant bits of all three color channels, red, green and blue, achieving three bits per pixel. That tripling of payload means a cover image can carry substantially more secret data before the sender must reach for a larger photograph, and larger covers themselves can attract attention. The trade-off, of course, is that modifying three bits per pixel distorts the cover more than modifying one, so the scheme must demonstrate that the resulting images still look and measure as natural. The authors report that the approach balances imperceptibility, visual fidelity and embedding capacity, preserving the perceptual quality of the original images even at the full three-bit payload.

Measuring that preservation requires standardized image quality metrics, and the study leans on the established toolkit of the field. Peak signal-to-noise ratio, or PSNR, quantifies the numerical difference between the cover and stego images, with higher values indicating smaller distortion; values above roughly 40 decibels are conventionally considered evidence that modifications are visually negligible. The structural similarity index, SSIM, goes further by modeling how human vision judges changes in luminance, contrast and structure, while the feature similarity index, FSIM, compares low-level visual features. The literature the authors cite, including comparative studies of these metrics and analyses of their use specifically in steganography evaluation, reflects a consensus that imperceptibility must be assessed both numerically and perceptually, since a stego image that passes one metric can still fail another.

Imperceptibility alone is not enough, because a hidden message that survives human scrutiny may still betray itself to statistical analysis. The authors evaluated their scheme against three classic detection tests. Visual inspection confirmed that stego images show no discernible artifacts. Histogram analysis examined the distribution of pixel intensities: natural images have characteristic, often smooth histograms, and naive embedding tends to produce telltale pairing effects in which adjacent intensity values converge in frequency. Chi-square distance tests, rooted in the statistical method Karl Pearson developed in the nineteenth century, compare the observed distribution of least significant bit values against the expectations of a natural image, flagging images whose bit planes look artificially uniform. The experimental evaluations showed that the proposed method withstands detection through all three tests, and comparative analyses with existing techniques further validated its robustness and reliability in concealing information while preserving visual fidelity.

The design choices sit within a rich research lineage that the paper’s bibliography traces in detail. Early combinations of cryptography and pseudorandom numbers for steganography date back more than a decade, and subsequent work refined random pixel selection, explored interpolation-based embedding, pursued lowest-distortion LSB schemes, and brought in error-correcting codes such as Hamming codes to raise capacity adaptively. More recent contributions have applied genetic algorithms to cover image selection, combined random pixel and bit selection for high payloads, and embedded encrypted text within color images using randomized pixel ordering. Parallel work in the same journal has explored chaotic maps for image ciphering and new high-capacity, high-security steganographic schemes. Against this backdrop, the Shillong study’s use of a lightweight, well-understood generator like XORShift64 is a pragmatic argument: security through unpredictability does not require exotic mathematics, only a well-integrated source of pseudorandomness and careful attention to where the bits go.

The practical implications extend to any scenario in which the confidentiality of transmitted images is paramount. The authors point to sensitive image exchange and information protection as the applications where the technique’s strong concealment capability and computational efficiency show the most promise. In an era when the paper’s own framing describes escalating cybersecurity threats driven by increasingly sophisticated attack techniques and a growing reliance on the internet for extensive data sharing, covert channels embedded in ordinary media offer a complementary layer of defense: even if an encrypted file is intercepted, an observer cannot be sure that a routine photograph does not also carry a hidden payload. The method’s low computational cost makes it suitable for embedding and extraction on everyday hardware, and its three-bit-per-pixel capacity reduces the number of cover images needed for a given volume of secret data.

Like all spatial-domain LSB methods, the approach inherits the field’s standing caveats. The study reports resistance to visual, histogram and chi-square analyses, but the steganalysis arms race continues, with machine-learning detectors constantly improving at recognizing even faint embedding traces, and robustness against image transformations such as compression or resizing remains a separate question from undetectability. The authors’ contribution is nonetheless a clear demonstration of a principle that has governed information hiding since Claude Shannon laid the mathematical foundations of communication theory in 1948: uncertainty in the mind of the adversary is a resource, and it can be manufactured cheaply. By spending a few bitwise operations per pixel to decide where each secret bit lands, the XS-RPVI framework turns one of the oldest tricks in digital steganography into a harder target for the tools that hunt it, and it does so while leaving the photograph, to every eye and every standard test, exactly what it seems.

Subject of Research: Image steganography using XORShift64-based random pixel selection for secure data hiding in color images

Article Title: Image steganography: emphasizing bit encoding positions for secure communication

Article References: Jamatia, R., & Bhuyan, B. (2026). Image steganography: emphasizing bit encoding positions for secure communication. Multimedia Tools and Applications, 85(10), Article 806. https://doi.org/10.1007/s11042-026-21962-8

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21962-8

Keywords: steganography, image steganography, LSB embedding, XORShift64, pseudorandom number generator, information hiding, steganalysis, chi-square test, PSNR, SSIM, data security, spatial domain