Pith. sign in

REVIEW 4 major objections 4 minor 33 references

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that a shared-seed pseudorandom pixel-selection order added to Adaptive Pixel Value Differencing (APVD) resolves the 'unused blocks' problem and yields a steganography method with higher security, higher embedding…

desk verdict APVD plus pseudorandom pixel selection, clearly described but with the capacity/security payoff unmeasured and likely illusory; reject. read the letter →

arxiv 2507.13367 v1 pith:WQMVSAHK submitted 2025-07-08 cs.CR cs.CVcs.MMeess.IV

Mehrab Hosain , Rajiv Kapoor This is my paper · ORCID
classification cs.CRcs.CVcs.MMeess.IV
keywords APVDimageprocessingLSBpseudorandomsequencesecuritysteganographyunusedblocksproblemqualitymetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that combining Adaptive Pixel Value Differencing (APVD) with pseudorandom pixel selection fixes the 'unused blocks' problem that limits ordinary APVD steganography. In the proposed scheme, a pseudorandom number generator seeded by a shared key decides the order in which pixel pairs are chosen for embedding, spreading modifications across more pairs and making the embedding pattern unpredictable. The paper reports PSNR values around 54 dB for grayscale covers and 53 dB for color covers, with SSIM and UIQ above 0.98, and presents these as evidence that the method beats established techniques in visual quality, embedding capacity, and security. If the central claim holds, the result is a lightweight, training-free steganography scheme that works on both color and grayscale images with only a seed shared in advance.

What carries the argument

The load-bearing mechanism is the pseudorandom pixel-pair sequence generated by a pseudorandom number generator initialized with a shared seed, $\text{PRNG}(S_{seed})$. The generator produces the sequence $P_1 = \text{PRNG}(S_{seed})$, $P_2 = \text{PRNG}(P_1)$, and so on up to $P_n$, fixing the order in which pixel pairs are visited during both embedding and extraction; because sender and receiver share the seed, extraction reproduces the same order. On each selected pair the underlying APVD logic applies: compute the pixel-value difference $D = |P_2 - P_1|$, map $D$ through the range function $k = f(D)$ to get the number of bits to embed, and modify the pair's values accordingly. For color covers the same operation runs independently on the R, G, and B channels. The mechanism's role is to perform ordinary APVD while spreading the changes unpredictably across the image, which the paper argues is what mitigates the unused-blocks and fall-off-boundary problems.

What would settle it

Use the same cover images and the same secret payload with ordinary APVD and with the pseudorandom-order APVD, then count how many distinct pixel pairs are actually used for embedding in each pass and measure the bits embedded per pair; if the pseudorandom order does not use a strictly larger set of distinct pairs, the unused-blocks mechanism is refuted. Then run a standard steganalysis detector (histogram analysis, RS analysis, or a trained steganalysis network) on both sets of stego-images at equal payloads: if detection rates are statistically indistinguishable, the claimed security advantage is not supported.

Watch

Extended reading notes

Core claim

The central claim is that APVD combined with pseudorandom pixel selection is a highly effective steganography technique, offering enhanced security, higher embedding capacity, and better visual quality preservation than established techniques. The authors' framing is that randomizing the traversal order attacks the unused-blocks problem directly: the pseudorandom sequence recruits pixel pairs that a fixed raster scan would skip, increasing capacity, while the unpredictable placement of changes makes the embedding pattern harder to detect and harder to reverse-engineer. On the reported evidence the method 'consistently achieves elevated PSNR, SSIM, and UIQ values for both color and grayscale images' (Section 6), with numbers that exceed those of the quantum substitution-box method used as the comparison baseline in Table 2 and Table 3. The authors also claim the approach is versatile, accommodating a variety of cover and secret images in color and grayscale without compromising the cover's visual quality.

Load-bearing premise

The load-bearing premise is that a pseudorandom travel order actually uses pixel pairs that the ordinary left-to-right scan leaves unused; the paper assumes this without ever counting the pairs, and if the count is unchanged the method is just ordinary APVD in a different order, with no real gain in capacity or security.

Editorial extensions

If this is right

  • Embedding capacity rises because the pseudorandom traversal recruits pixel pairs that a fixed raster scan leaves unused, letting the same cover carry a larger secret message.
  • Security improves because without the shared seed the embedding order is unpredictable, making stego-image analysis and reverse-engineering harder.
  • Visual quality stays high at the reported levels (PSNR about 54 dB for grayscale and 53 dB for color covers, SSIM and UIQ above 0.98), so the capacity gain does not come at the cost of visible distortion.
  • Extraction is seed-synchronized, so the scheme works as a keyed protocol: anyone without the seed cannot reconstruct the embedding order and thus cannot locate the hidden data.
  • The same pipeline handles grayscale and color covers (per-channel for RGB), so the method applies to common image formats without additional machinery.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The missing experiment is a direct count of distinct pixel pairs used for embedding under the ordinary raster scan versus the pseudorandom traversal on identical covers; the paper asserts the traversal recruits unused blocks but never measures it, and that count would settle whether the capacity gain is real.
  • The security claim is argued but not measured; applying histogram or RS steganalysis to the generated stego-images would give a quantitative test, and any true advantage should show up as harder-to-locate pair positions rather than different pixel-value statistics.
  • The comparison baseline is a single quantum substitution-box method; running the same protocol against the other listed approaches (LSB matching, DCT-based, deep-learning hiding) under matched payloads would reveal whether the reported margins generalize.
  • Because selection is seed-driven, the machinery permits content-aware orderings (for instance, visiting high-difference pairs first) without any change to the extraction protocol — a direction the paper leaves unexplored.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a steganography scheme that combines Adaptive Pixel Value Differencing (APVD) with pseudorandom pixel-pair selection. The core claim is that pseudorandom traversal alleviates the "unused blocks" problem of APVD, thereby increasing embedding capacity and security while preserving visual quality. The manuscript provides a high-level embedding/extraction procedure, reports PSNR, SSIM, and UIQ values for four grayscale and four color test images, and concludes that the method outperforms a quantum-S-box-based steganography method from [1]. No capacity, payload, used-block, or steganalysis experiments are reported.

Significance. If the central mechanism were validated, the idea of using a pseudorandom traversal order to improve APVD would be a modest but plausible contribution to image steganography. The manuscript, however, does not validate the mechanism: the PRNG-to-pair mapping is ambiguous, the claim about recruiting unused blocks is not justified for a fixed set of non-overlapping pairs, and the experimental section contains no capacity or security measurements. The tabulated quality metrics for eight images are a useful starting point for reproducibility, but they do not support the paper's headline claims. The significance of the work as presented is therefore low.

major comments (4)
  1. [Section 3.2, 3.6] The paper's central claim in Section 3.6 that pseudorandom selection 'increases the chances of utilizing a higher number of pixel pairs' is not supported by the described method. In APVD, the usability of a pixel pair is determined by the local difference D through Eqs. (1)-(2), independent of the order in which pairs are visited. If the PRNG merely permutes a fixed partition of non-overlapping pairs, the set of usable pairs and hence the embedding capacity is unchanged. If the PRNG instead selects pairs dynamically, the extraction procedure in Section 3.3 cannot reliably regenerate the same pairs from the stego image because the recurrence in Eq. (4) is not a well-defined coordinate-generation rule and the notation (P1,P2)=PRNG(Sseed,i) in Section 3.3 is inconsistent with it. No experiment reports used-block counts or embedding rate, so the claimed remedy for the unused-blocks problem is untested.
  2. [Section 5, Tables 2-3] Tables 2 and 3 report only PSNR, SSIM, and UIQ between cover and stego images. The concluding claims of 'higher embedding capacity' and 'enhanced security' are not supported by any reported capacity, payload, or steganalysis measurement. Section 7 even lists the development of steganalysis tests as future work, acknowledging their absence. In addition, the comparison with the quantum S-box method [1] is made only in prose; no baseline numbers from [1] are tabulated, so the claimed superiority cannot be verified.
  3. [Section 3.1, 3.2, Eqs. (2)-(4)] Eq. (2) defines the embedding capacity as k=f(D), but the range table f is never specified, so the actual per-pair capacity is undefined and the reported PSNR, SSIM, and UIQ results cannot be reproduced. Similarly, the pseudorandom generator interface is unclear: Eq. (3) suggests PRNG(Sseed)->Pi, while Eq. (4) iterates P_n = PRNG(P_{n-1}), which mixes a seed with a pixel-pair state without defining how a pair becomes a PRNG state. A precise algorithm (e.g., a seeded permutation of coordinates) is needed for both embedding and extraction.
  4. [Section 3.7, Eqs. (7)-(8)] Equations (7) and (8) contain scrambled variables and subscripts (e.g., 'μμμμ', 'σμμμμ', 'μμμ2') that do not match the standard SSIM or UIQ formulas. Since the entire experimental evaluation rests on these metrics, the formulas must be corrected to the published definitions. The typographical issues here are not merely cosmetic; they prevent verification of the reported quality values.
minor comments (4)
  1. [Section 4, Section 5] Section 4 states that image sizes are 256x256, while Section 5 says covers were resized to 512x512; the preprocessing steps and final sizes used in the evaluation should be stated consistently.
  2. [Tables 2 and 3] The PSNR values are nearly identical across different secret images (e.g., 54.17, 54.17, 54.18 for the Elaine cover); the authors should clarify whether values are rounded and why the secret image identity has negligible effect.
  3. [General] The manuscript contains many grammatical and typographical errors ('shows a crucial role', 'a several of techniques', 'assess the PSNR' as table headings, 'the proposed method's excellence') that require copyediting.
  4. [Fig. 1] Fig. 1 is referenced as a 'diagrammatic representation' but the figure itself is not described in the text; a detailed caption explaining the embedding and retrieval data flow should be added.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the paper's weaknesses are unverified empirical claims, not circular reductions.

full rationale

The paper contains no fitted equations and makes no quantitative predictions that are derived from measured parameters; consequently there is no derivation chain whose output could be equivalent to its input. The proposed method composes two existing ingredients: APVD capacity selection through D=|P2-P1| and k=f(D) (Eqs. 1-2), and a pseudorandom traversal of pixel pairs driven by PRNG(Sseed) (Eqs. 3-4). These are independent mechanisms, and the reported PSNR/SSIM/UIQ values (Tables 2-3) are standard external quality metrics, not quantities defined by the method's own outputs. The central weakness is that the Section 3.6 assertion that pseudorandom selection 'increases the chances of utilizing a higher number of pixel pairs for embedding' and thereby enhances capacity and security is never measured; no block-usage count, payload measurement, or steganalysis resistance test is reported. That is an evidentiary gap, not a circular reduction: the claim does not follow by construction from the equations, and no fitted parameter is relabeled as a prediction. The paper also does not rely on any self-citation for its central premise; the cited APVD and pseudorandom-selection works are external and their results are not used to derive the claimed improvements. The inconsistency between Eq. (4) and the per-pair PRNG call in Section 3.3, and the omission of baseline values for the quantum S-box comparison, are reproducibility and correctness concerns, not instances of circularity. On the definitions in the rubric, the appropriate finding is therefore 'no significant circularity' with score 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper contributes an empirical evaluation, so its ledger is dominated by assumptions imported from prior work: the APVD range table is taken from [16] without being stated, the PRNG behavior is assumed reproducible, and the security gain from pseudorandom ordering is asserted rather than measured.

free parameters (2)
  • PRNG seed S_seed
    Shared secret for generating the pixel-pair selection sequence; the value is never given and different seeds would change results. It is a hand-chosen input rather than a fitted value.
  • APVD range table k=f(D)
    The difference-to-capacity mapping is taken from Luo et al. [16] but not stated in this paper; the reported performance depends on this chosen mapping.
assumptions (4)
  • domain assumption The APVD embedding and extraction operations cited from Luo et al. [16] are correct and reversible.
    The proposed method relies entirely on APVD for embedding and extraction; the paper does not restate the range table or the modification rule.
  • domain assumption Pseudorandom traversal of pixel pairs mitigates the unused blocks problem and raises embedding capacity and security.
    Section 3.6 asserts these benefits without empirical support; the entire improvement claim rests on this.
  • domain assumption The pseudorandom pixel-pair sequence is reproducible from the shared seed at extraction time.
    The extraction process assumes the same pair selection order; the paper gives chained and seed-index PRNG notations that are not reconciled.
  • domain assumption PSNR, SSIM, and UIQ are sufficient indicators of steganographic quality and security.
    The paper uses only image quality metrics to support security claims; no steganalysis or statistical tests are run.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security." pith.science (2026). https://pith.science/paper/WQMVSAHK

@misc{pith2026250713367,
  author       = {Pith},
  title        = {Pith review of: A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WQMVSAHK}},
  note         = {Machine review of arXiv:2507.13367}
}
read the original abstract

Steganography is the process of embedding secret information discreetly within a carrier, ensuring secure exchange of confidential data. The Adaptive Pixel Value Differencing (APVD) steganography method, while effective, encounters certain challenges like the "unused blocks" issue. This problem can cause a decrease in security, compromise the embedding capacity, and lead to lower visual quality. This research presents a novel steganographic strategy that integrates APVD with pseudorandom pixel selection to effectively mitigate these issues. The results indicate that the new method outperforms existing techniques in aspects of security, data hiding capacity, and the preservation of image quality. Empirical results reveal that the combination of APVD with pseudorandom pixel selection significantly enhances key image quality metrics such as Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQ), and Structural Similarity Index (SSIM), surpassing other contemporary methods in performance. The newly proposed method is versatile, able to handle a variety of cover and secret images in both color and grayscale, thereby ensuring secure data transmission without compromising the aesthetic quality of the image.

Figures

Figures reproduced from arXiv: 2507.13367 by the authors.

Figure 2
Figure 2. Images of the Proposed Steganographic Process In [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. The figure showcases four examples of visual results for grayscale images: the first column presents the cover images, the second column exhibits the stego images, the third column visually represents the secret images, and the fourth column unveils the images that contain the extracted secrets [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. The figure provides four instances of visual outcomes for color images: the first column introduces the cover images, the second column brings forward the stego images, the third column reveals the secret images, and the fourth column brings to light the images containing the extracted secrets [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 25 canonical work pages

  1. [16]

    Multimed Tools Appl 52:407–430

    Luo W, Huang F, Huang J (2011) A more secure steganography based on adaptive pixel- value differencing scheme. Multimed Tools Appl 52:407–430. doi: 10.1007/s11042-009- 0440-3

    doi:10.1007/s11042-009- 2011

  2. [20]

    IEEE TransInformForensic Secur 5:225–239

    Sarkar A, Madhow U, Manjunath BS (2010) Matrix Embedding With Pseudorandom Coefficient Selection and Error Correction for Robust and Secure Steganography. IEEE TransInformForensic Secur 5:225–239. doi: 10.1109/TIFS.2010.2046218

    arXiv 2010

  3. [8]

    In: 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP)

    Hadis MS, Akita J, Toda M, Nurzaenab (2022) The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement. In: 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, Sofia, Bulgaria, pp 1–5

    work page 2022

  4. [1]

    Optics & Laser Technology 116:92–102

    Abd EL -Latif AA, Abd -El-Atty B, Venegas -Andraca SE (2019) A novel image steganography technique based on quantum substitution boxes. Optics & Laser Technology 116:92–102. doi: 10.1016/j.optlastec.2019.03.005

    work page doi:10.1016/j.optlastec.2019.03.005 2019

  5. [2]

    In: Wong PW, Delp III EJ (eds)

    Avcibas I, Memon ND, Sankur B (2001) Steganalysis of watermarking techniques using image quality metrics. In: Wong PW, Delp III EJ (eds). San Jose, CA, pp 523–531

    work page 2001

  6. [3]

    Pattern Recognition 37:469–474

    Chan C-K, Cheng LM (2004) Hiding data in images by si mple LSB substitution. Pattern Recognition 37:469–474. doi: 10.1016/j.patcog.2003.08.007

    doi:10.1016/j.patcog.2003.08.007 2004

  7. [4]

    J Comput Sci Technol 37:1134–1145

    Deng X-Q, Chen B-L, Luo W -Q, Luo D (2022) Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling. J Comput Sci Technol 37:1134–1145. doi: 10.1007/s11390-021-0572-0

    work page doi:10.1007/s11390-021-0572-0 2022

  8. [5]

    In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

    Fratila R, Morogan L (2021) Enhanced models in deep image steganography. In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, Pitesti, Romania, pp 1–4

    work page 2021

Show all 33 references
  1. [6]

    Cambridge University

    Fridrich J (2010) Steganography in digital media: principles, algorithms, and applications, First. Cambridge University

    2010

  2. [7]

    IEEE Multimedia 8:22–28

    Fridrich J, Goljan M, Rui Du (2001) Detecting LSB steganography in color, and gray- scale images. IEEE Multimedia 8:22–28. doi: 10.1109/93.959097

    2001 doi

  3. [9]

    Journal of Visual Communication and Image Representation 49:351–360

    He W, Zhou K, Cai J, Wang L, Xiong G (2017) Reversible data hiding using multi -pass pixel value ordering and prediction-error expansion. Journal of Visual Communication and Image Representation 49:351–360. doi: 10.1016/j.jvcir.2017.10.001

    2017 doi

  4. [10]

    Hore A, Ziou D (2010) Image Quality Metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition. IEEE, Istanbul, Turkey, pp 2366–2369

    2010

  5. [11]

    Computer 31:26–34

    Johnson NF, Jajodia S (1998) Exploring steganography: Seeing the unseen. Computer 31:26–34. doi: 10.1109/MC.1998.4655281

    1998

  6. [12]

    Journal of Information Processing Systems 9:405–

    Juneja M, Sandhu PS (2013) A New Approach for Information Security using an Improved Steganography Technique. Journal of Information Processing Systems 9:405–

    2013

  7. [13]

    In: Bhateja V, Satapathy SC, Travieso-Gonzalez CM, Flores-Fuentes W (eds) Computer Communication, Networking and IoT

    Khare P (2021) A Novel Image Functioned Framework for Steganography Using PS-DCT Approach. In: Bhateja V, Satapathy SC, Travieso-Gonzalez CM, Flores-Fuentes W (eds) Computer Communication, Networking and IoT. Springer Singapore, Singapore, pp 73– 84

    2021

  8. [14]

    In: Khare A, Tiwary US, Sethi IK, Singh N (eds) Recent Trends in Communication, Computing, and Electronics

    Khosravy M, Patel N, Gupta N, Sethi IK (2019) Image Quality Assessment: A Review to Full Reference Indexes. In: Khare A, Tiwary US, Sethi IK, Singh N (eds) Recent Trends in Communication, Computing, and Electronics. Springer Singapore, Singapore, pp 279– 288

    2019

  9. [15]

    International Journal of Advanced Research in Computer Science and Software Engineering 3:7

    Kumar A, Sharma R (2018) A secure image Steganography based on RSA Algorithm and Hash-LSB technique. International Journal of Advanced Research in Computer Science and Software Engineering 3:7

    2018

  10. [17]

    Pandey D, Wairya S, Al Mahdawi R, Najim SA, Khalaf H, Al Barzinji S, Obaid A (2021) Secret data transmission using advanced steganography and image com pression. IJNAA

    2021

  11. [18]

    Security and Communication Networks 2017:1–13

    Pradhan A, Sekhar KR, Swain G (2017) Adaptive PVD Steganography Using Horizontal, Vertical, and Diagonal Edges in Six-Pixel Blocks. Security and Communication Networks 2017:1–13. doi: 10.1155/2017/1924618

    2017 doi

  12. [19]

    doi: 10.22075/ijnaa.2021.5635 13

    2021

  13. [21]

    IEEE Secur Privacy 1:32–44

    Provos N, Honeyman P (2003) Hide and seek: an introduction to steganography. IEEE Secur Privacy 1:32–44. doi: 10.1109/MSECP.2003.1203220

    2003 arXiv

  14. [22]

    International Journal of Advanced Research in Computer Science and Software Engineering 2:29–33

    Singh M, Sharma R, Garg D (2012) A New Purposed Issue for Secure Image Steganography Technique Based On 2-D Block DCT and DCT. International Journal of Advanced Research in Computer Science and Software Engineering 2:29–33

    2012

  15. [23]

    International Journal of Computing and Business Research (IJCBR) 2:16

    Sharma R, Ekta W, Sharma D (2011) ANALYSIS OF NON -ADAPTIVE AND ADAPTIVE EDGE BASED LSB STEGANOGRAPHY FOR COLORED IMAGES. International Journal of Computing and Business Research (IJCBR) 2:16

    2011

  16. [24]

    Soft Comput 25:10987–11004

    Tang L, Wu D, Wang H, Chen M, Xie J (2021) An adaptive fuzzy inference approach for color image steganography. Soft Comput 25:10987–11004. doi: 10.1007/s00500-021- 05825-y

    2021 doi

  17. [25]

    Wireless Pers Commun 123:273–293

    Swain G, Pradhan A (2022) Image Steganography Using Remainder Replacement, Adaptive QVD and QVC. Wireless Pers Commun 123:273–293. doi: 10.1007/s11277- 021-09131-6

    2022 doi

  18. [26]

    Signal Processing: Image Communication 101:116556

    Tseng H-W, Leng H-S (2022) A reversible modified least significant bit (LSB) matching revisited method. Signal Processing: Image Communication 101:116556. doi: 10.1016/j.image.2021.116556

    2022

  19. [27]

    IEEE Signal Process Lett 24:1547–1551

    Tang W, Tan S, Li B, Huang J (2017) Automatic Steganographic Distortion Learning Using a Generative Adversarial Network. IEEE Signal Process Lett 24:1547–1551. doi: 10.1109/LSP.2017.2745572

    2017

  20. [28]

    J Phys: Conf Ser 2337:012009

    Yang P, Zhang M, Wu R, Su Y, Guo K (2022) Hiding Image within Image Based on Deep Learning. J Phys: Conf Ser 2337:012009. doi: 10.1088/1742-6596/2337/1/012009

    2022 doi

  21. [29]

    In: Pfitzmann A (ed) Information Hiding

    Westfeld A, Pfitzmann A (2000) Attacks on Steganographic Systems. In: Pfitzmann A (ed) Information Hiding. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 61–76

    2000

  22. [30]

    J of Applied Sciences 10:1650–1655

    Zaidan BB, Zaidan AA, Al -Frajat AK, Jalab HA (2010) On the Differences between Hiding Information and Cryptography Techniques: An Overview. J of Applied Sciences 10:1650–1655. doi: 10.3923/jas.2010.1650.1655

    2010

  23. [31]

    Multimed Tools Appl 82:10835–10866

    Yang Y -G, Wang B-P, Zhou Y -H, Shi W -M, Liao X (2023) Efficient color image encryption by color-grayscale conversion based on steganography. Multimed Tools Appl 82:10835–10866. doi: 10.1007/s11042-022-13689-z

    2023 doi

  24. [33]

    Pattern Recognition Letters 25:331–339

    Zhang X, Wang S (2004) Vulnerability of pixel -value differencing steganography to histogram analysis and modification for enhanced security. Pattern Recognition Letters 25:331–339. doi: 10.1016/j.patrec.2003.10.014

    2004 doi

  25. [424]

    doi: 10.3745/JIPS.2013.9.3.405

    2013 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.