All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
The authors properly addressed the requests and suggestions of the reviewers.
[# PeerJ Staff Note - this decision was reviewed and approved by Vicente Alarcon-Aquino, a PeerJ Section Editor covering this Section #]
in the new version, article was very improved
in the new version, article was very improved
in the new version, article was very improved
Please take precautions to go through all criticisms.
**PeerJ Staff Note:** It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors agree that they are relevant and useful.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
The authors propose a new reversible visual cryptosystem for secure and efficient transmission of medical images in the Internet of Things. It distinguishes itself from traditional image encryption algorithms by using secret image sharing techniques and information hiding to generate visually secure cipher images, which brings good security. However, there are still some problems to be solved.
(1) Many of the references in the manuscript are too old, and it is recommended that they be replaced with the latest references, e.g., 2024, 2025. In addition, check for missing or inconsistent formatting of reference information.
(2) Is there a limit to the use of this method during the shared generation phase? For example, image size, range of pixel values, etc.
(3) Is the anomaly symbol in the paragraph following Table 1 a writing error?
-
(4) Add histogram analysis and correlation analysis.
(5) You may add comparative analysis with similar algorithms. For example,
a. doi.org/10.1016/j.chaos.2023.114111
b. doi.org/10.1007/s00530-025-01765-x
c. doi.org/10.1016/j.chaos.2024.115632
**PeerJ Staff Note:** It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful.
The manuscript is well-written in clear, professional English, with only minor grammatical and formatting issues that do not hinder comprehension. The introduction effectively contextualizes the need for secure image transmission in IoT-based healthcare, and the motivation for the proposed Inverse Modular Visual Cryptography (IMVC) system is well articulated. Relevant prior work is cited appropriately, and the literature review provides a solid theoretical foundation. Figures and tables are of high quality, clearly labeled, and support the narrative effectively, while the inclusion of publicly available implementation code and datasets ensures transparency and reproducibility. The manuscript follows PeerJ’s structural guidelines, with a logical flow from introduction to conclusion. Minor improvements in formatting, figure-text alignment, and proofreading would further enhance clarity and presentation.
This is my comment
1. Could the authors elaborate on how key selection (e.g., modulus size) impacts security
and decryption fidelity in resource-constrained devices?
2. Could real-world noise (e.g., transmission distortion or compression artifacts) affect
these perfect scores? If so, have simulations been tested?
3. Did the authors explore the visual detectability or steganalysis resilience of these
shares? Could such attacks distinguish between cover from share images?
This is my comment
1. Could partial manipulation of a share (not a full fake) still degrade output quality? How
tolerant is the reconstruction process to minimal corruption?
2. It would be useful to highlight whether the GitHub code includes visualization outputs
like PSNR/SSIM plots and fake share detection.
3. For future work, consider applying this scheme to full medical workflows (e.g.,
DICOM files, PACS systems) and extending to 3D imaging datasets.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.