Enhanced edge-based steganography using image segmentation and random LSB substitution for secure data hiding

Detector	Noise sensitivity	Edge thickness	Comp. cost	Key reference
Sobel	Medium	Thick	Low	Sun et al. [5]
Prewitt	Medium	Thick	Low	Sun et al. [5]
Laplacian	High	Thick but noisy	Low–Med	Habiban et al. [8]
Canny	Low	Thin, connected	Med–High	Mohanty et al. [6] and Zhang et al. [7]

Table 2:

Existing steganographic techniques: contributions and limitations

S. No.	Reference	Contribution	Limitation
1	[3]	Multilevel encryption + blue-channel block shuffling; near-zero MSE, SSIM	Vulnerable to geometric distortions and lossy compression.
2	[2]	LSB + PVD + multitier encryption; PSNR > 30 dB, SSIM ≈1	Trade-offs between payload, imperceptibility, and robustness not fully explored.
3	[13]	Center-pixel offset + pseudorandom sequence; high PSNR, low cost	Untested against advanced steganalysis.
4	[8]	LoG + Canny hybrid in wavelet domain; high capacity, strong imperceptibility	Limited payload when edges are sparse.
5	[10]	Survey of chaotic and randomized	Increased computational complexity.
6	[14]	LSB strategies; improved stealth MSB matching + LSB substitution; up to 6 bits/pixel, minimal distortion	Resilience under compression not evaluated.
7	[15]	Distinction grade value method; robust against noise	Lacks new experimental validation.
8	[16]	Embedding in high-variance regions; PSNR/SSIM gains	Computationally intensive for real-time use.

[i] LSB, least significant bit; MSE, mean squared error; PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

Histogram analysis of cover and stego-image.

Table 3:

Evaluation metrics for stego-image quality and performance

Metric	Description	Role
Capacity (m)	The total number of bits that can be embedded into the cover image; mathematically m = \|M\|, where M is the message bit-stream.	Defines payload limit and governs the trade-off between embedding rate and imperceptibility.
MSE	MSE between cover and stego images: $MSE = \frac{1}{MN} \sum_{i = 1}^{M} \sum_{j = 1}^{N} {(C (i, j) - S (i, j))}^{2} .$	Quantifies raw embedding noise energy; lower MSE indicates smaller average pixel perturbations.
PSNR	PSNR, computed from MSE: $PSNR = 10 \cdot {log}_{10} (\frac{255^{2}}{MSE}) .$	Transforms MSE into a more interpretable logarithmic scale; values > 40 dB typically imply perceptual losslessness.
SSIM	Structural Similarity Index, measuring perceptual quality: $SSIM (C, S) = \frac{(2 μ_{C} μ_{S} + C_{1}) (2 σ_{C S} + C_{2})}{(μ_{C}^{2} + μ_{S}^{2} + C_{1}) (σ_{C}^{2} + σ_{S}^{2} + C_{2})},$ where μ, σ, and σ_CS denote means, variances, and covariance, respectively.	Assesses structural and contrast preservation; high SSIM (≈1) ensures imperceptibility and resists structure-based attacks.
Embedding time (T_e)	Total time for the embedding operation:; $T_{e} = T_{pre} + T_{embed},$ where T_pre is preprocessing (e.g., edge detection) and T_embed is bit embedding.	Measures computational efficiency; critical for real-time or large-scale applications.
Histogram analysis	Comparison of pixel-intensity histograms between cover and stego images to detect distribution shifts.	Detects statistical anomalies introduced by embedding; ensures stego-images remain statistically indistinguishable from covers.

[i] MSE, mean squared error; PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

Algorithm 1:

Proposed algorithm

Require: Cover image C, Message image M
Ensure: Stego image S
- 1: Step 1: Preprocessing and Image Segmentation
- 2: Load C and M
- 3: if C and M are color images then
- 4: Convert them to grayscale using [32]:
  $G (i, j) = 0.2989 R (i, j) + 0.5870 G (i, j) + 0.1140 B (i, j)$
- 5: end if
- 6: Apply threshold T for binarization:
  $B (i, j) = \begin{array}{l} 1, & if G (i, j) > T \\ 0, & otherwise \end{array}$
- 7: Use Otsu's method to calculate T:
  $T = arg {max}_{T} σ_{B}^{2} (T)$
  where $σ_{B}^{2} (T)$ is the between-class variance.
- 8: Perform segmentation on B_C(x, y) and B_M (x, y).
- 9: Step 2: Edge Detection
- 10: Apply Canny edge detection on B_C(x, y) [33]:
  $| \nabla I | = \sqrt{{(\frac{\partial I}{\partial X})}^{2} + {(\frac{\partial I}{\partial Y})}^{2}}$
- 11: Retain edge pixels whose lengths match edges in B_M (i, j).
- 12: Step 3: Randomization and LSB Substitution
- 13: Randomly select edge pixels using a pseudo-random number generator RNG with secret key K [34]:
  $Q (j) = RNG (E_{C} (i, j), K)$
- 14: Perform LSB substitution:
  $Q^{'} (i, j) = \begin{array}{l} Q (i, j) - (Q (i, j) \mod 2) + b, & if embedding bit b = 1 \\ Q (i, j) - (Q (i, j) \mod 2), & if b = 0 \end{array}$
- 15: Step 4: Stego Image Generation
- 16: Combine modified edge pixels with non-edge regions to form S(i, j).
- 17: Step 5: Post-Processing and Quality Metrics
- 18: Compute PSNR [35]:
  $PSNR = 10 \cdot {log}_{10} (\frac{{MAX}^{2}}{MSE})$
  where $MSE = \frac{1}{MN} \sum_{i = 1}^{M} \sum_{j = 1}^{N} {(C (i, j) - S (i, j))}^{2}$
- 19: Accept S only if PSNR > 40.
- 20: Step 6: Embedding Time
- 21: Measure embedding time T_e [26]:
  $T_{e} = T_{pre} + T_{embed}$
- 22: Optimize T_e by refining edge detection and randomization steps.

Table 4:

Evaluation metrics of different cover and message images

S. No.	Cover image	Message image	Capacity (BPP)	PSNR (dB)	SSIM	Embedding time (s)
1	Moon	Lena	0.0358	65.60	1.0000	0.2441
2	Barb	Lena	0.0524	63.95	1.0000	0.0613
3	Bluesea	Lena	0.0225	67.63	1.0000	0.1500
4	Boat	Lena	0.0288	66.82	1.0000	0.0126
5	House	Lena	0.0395	65.19	1.0000	0.2096
6	Loin	Lena	0.0355	65.65	1.0000	0.0286
7	Mandrill	Lena	0.0394	65.18	1.0000	0.0144
8	Nature	Lena	0.0384	65.30	1.0000	0.3303
9	River	Lena	0.0440	64.69	1.0000	0.0690
10	Water	Lena	0.0488	64.25	1.0000	0.2434

[i] PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

Table 5:

Results of detected edges of stego-image with SSIM value

[i] SSIM, structural similarity index measure.

Table 6:

Performance comparison of embedding strategies

Embedding strategy	Average PSNR (dB)	Average SSIM	Embedding rate (BPP)
Edge-guided embedding	65.43	1.000	0.0385
Global embedding	62.99	0.962	0.0568

[i] PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

Table 7:

Comparison of PSNR and SSIM with recent LSB-based methods

No.	Reference	Baseline PSNR (dB)	Baseline SSIM	Our PSNR (dB)	Our SSIM
1	Ali et al. [14]	63.95	0.965	65.60	1.0000
2	Chaudhary and Sharma [34]	54.32	0.943	63.95	1.0000
3	Mali and Dongre [12]	65.19	0.972	65.30	1.0000
4	Mukherjee and Sanyal [36]	52.87	0.930	66.82	1.0000
5	Rahman et al. [3]	52.14	0.968	65.19	1.0000
6	Zhang et al. [7]	65.30	0.970	65.65	1.0000
7	Kaur and Singh [18]	64.69	0.960	65.18	1.0000
8	Wu and Li [37]	65.00	0.975	65.30	1.0000
9	Wang and Zhang [25]	66.00	0.978	67.63	1.0000

[i] LSB, least significant bit; PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

Table 8:

Average improvement in PSNR and SSIM

Metric	Average improvement (%)
PSNR improvement	3.24
SSIM improvement	3.95

[i] PSNR, peak signal-to-noise ratio; SSIM, structural similarity index measure.

References

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Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2025-0049 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Mar 30, 2025

Published on: Oct 17, 2025

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

edge detection,

image segmentation,

random LSB substitution,

Related subjects:

Introductions and overviews,

Engineering, other

© 2025 Juhi Singh, Arun Kumar Singh, Shishir Singh Chauhan, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 18 (2025): Issue 1 (January 2025)

Enhanced edge-based steganography using image segmentation and random LSB substitution for secure data hiding

Figures & Tables

Table 1:

Table 2:

Figure 1:

Figure 2:

Figure 3:

Table 3:

Algorithm 1:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

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