Developing a conference paper on Reversible Data Hiding (RDH) in 3D mesh models requires a structured approach to effectively communicate your research contributions. RDH techniques allow embedding data into a 3D model such that the original, unaltered model can be perfectly recovered after data extraction. This guide outlines the essential sections and content needed for a compelling paper.

Key Highlights

• Balancing Act: Successfully implementing RDH in 3D meshes involves navigating the critical trade-off between maximizing embedding capacity (how much data can be hidden) and minimizing visual distortion to the model.
• Perfect Fidelity: The core promise of RDH is complete reversibility – ensuring the original 3D mesh can be restored without any loss or modification after the hidden data is extracted.
• Security in the Cloud: A significant area of RDH research focuses on encrypted 3D models (RDH-ED), allowing data hiding even when the model's content is protected, crucial for cloud storage and secure transmission.

Structuring Your Conference Paper

A well-structured paper guides the reader logically through your research. Here’s a standard format with detailed considerations for RDH in 3D meshes:

1. Title and Abstract

Crafting Your First Impression

• Title: Make it concise, informative, and reflective of your specific contribution (e.g., "High-Capacity Adaptive RDH for Encrypted 3D Point Clouds").
• Abstract: A brief summary (typically 150-250 words) covering the problem, your proposed method, key experiments, results (highlighting capacity and distortion metrics), and the significance of your work.

2. Introduction

Setting the Stage

• Context: Start with the growing importance and applications of 3D models (engineering, gaming, medicine, virtual/augmented reality, manufacturing).
• Introduce RDH: Define Reversible Data Hiding, emphasizing its ability to embed data losslessly. Explain why it's valuable (authentication, metadata embedding, covert communication, copyright protection).
• The Challenge with 3D Meshes: Discuss the unique difficulties of applying RDH to 3D meshes compared to 2D images (complex topology, irregular vertex distribution, sensitivity to geometric distortion).
• Problem Statement: Clearly articulate the specific problem your research addresses (e.g., low embedding capacity in existing methods, high distortion, lack of robustness, specific challenges in encrypted domains).
• Contribution & Objective: Briefly introduce your proposed method and state its main advantages and the objectives of your study (e.g., to develop a novel RDH scheme with higher capacity and lower distortion for encrypted 3D meshes using adaptive prediction).
• Paper Outline: Briefly mention the structure of the rest of your paper.

3. Literature Review / Related Work

Building on Existing Knowledge

• Survey Existing RDH Techniques: Discuss established RDH methods, categorizing them:
  • Spatial Domain: Methods directly modifying mesh geometry (vertex coordinates, connectivity). Examples include Histogram Shifting (HS), Difference Expansion (DE), Prediction Error Expansion (PEE), Least Significant Bit (LSB) substitution variations, Multi-Most Significant Bit (MSB) prediction.
  • Compressed Domain: Methods embedding data within compressed representations of the 3D mesh (e.g., manipulating quantized coefficients).
  • Encrypted Domain (RDH-ED): Techniques designed for embedding data into encrypted 3D models, often using homomorphic encryption or specific reservation strategies before encryption. Mention approaches like reserving room in LSBs before encryption or using techniques compatible with encryption schemes (e.g., Paillier cryptosystem).
• Analyze Limitations: Critically evaluate existing methods, pointing out their drawbacks regarding embedding capacity, visual distortion (measured by metrics like PSNR, Hausdorff Distance), computational complexity, security vulnerabilities, or applicability to specific mesh types or encrypted scenarios.
• Position Your Work: Clearly explain how your proposed method addresses these limitations and differs from previous approaches. Cite key papers relevant to your specific techniques (e.g., papers on adaptive vertex grouping, closest pair prediction, multi-layer embedding, specific encryption schemes).

Understanding RDH Techniques

Reversible data hiding techniques for 3D meshes can be broadly categorized based on the domain they operate in and the specific strategy used for embedding. The mindmap below illustrates some common approaches:

Your proposed method will likely fall into one or a combination of these categories. Clearly explaining the underlying principles of your chosen approach is crucial.

Presenting Your Proposed Method

This section is the core of your paper, detailing your unique contribution.

4. Methodology / Proposed Scheme

Detailing Your Innovation

• Overview: Provide a high-level description of your method and its workflow. A flowchart or diagram can be very effective here.
• Mesh Representation: Specify the 3D mesh format you work with (e.g., triangle mesh, point cloud) and how geometric/topological information (vertices, faces, edges, normals) is utilized.
• Preprocessing (if any): Describe any steps taken before embedding, such as mesh partitioning, vertex ordering, or encryption (for RDH-ED). If using encryption, detail the algorithm (e.g., Paillier homomorphic encryption, stream cipher).
• Data Embedding Process: This is the most critical part. Explain step-by-step:
  • How embedding locations are selected (e.g., based on vertex prediction errors, histogram bins, specific bit-planes).
  • The mechanism for creating space (e.g., shifting histogram bins, expanding prediction errors, modifying LSBs/MSBs). Describe any adaptive strategies (e.g., adaptive vertex grouping based on local complexity, adaptive prediction based on neighbor geometry).
  • How the secret data bits are inserted into the created space.
  • How auxiliary information (e.g., overflow/underflow location map, prediction parameters, original LSBs) needed for reversibility is handled (e.g., compressed using arithmetic coding, embedded alongside payload).
  Provide mathematical formulations or algorithms where appropriate. For example, if using PEE, show the prediction formula and the expansion function.
• Data Extraction and Mesh Recovery Process: Clearly explain the inverse process:
  • How the receiver extracts the embedded data using the data hiding key.
  • How the auxiliary information is retrieved and used.
  • The exact steps to reverse the modifications and perfectly restore the original vertex coordinates or other modified attributes, proving the reversibility of your scheme.
• Security Considerations (especially for RDH-ED): Discuss how the confidentiality of the model (if encrypted) and the hidden data is maintained. Analyze resilience against potential attacks.

Evaluating Performance

Robust evaluation is key to demonstrating the effectiveness of your RDH scheme. Comparisons against baseline or state-of-the-art methods are essential.

5. Experimental Setup

Ensuring Reproducibility

• Datasets: Specify the 3D models used for testing (e.g., Stanford Bunny, Dragon, Armadillo; source like repositories, specific datasets). Mention model characteristics (number of vertices/faces, complexity).
• Evaluation Metrics: Clearly define the metrics used to assess performance:
  • Embedding Capacity (EC): Total bits embedded (payload size), often measured in bits per vertex (bpv) or bits per face (bpf).
  • Distortion: Quantify the difference between the original and marked (data-embedded) mesh. Common metrics include:
    • Peak Signal-to-Noise Ratio (PSNR) applied to vertex coordinates.
    • Hausdorff Distance (HD) measuring maximum geometric deviation.
    • Structural Similarity Index (SSIM) adapted for meshes, if applicable.
    • Visual inspection (include figures showing original vs. marked models, potentially highlighting differences).
  • Reversibility Check: Confirm that the restored mesh is identical to the original (e.g., bit-wise comparison of coordinates).
  • Computational Complexity: Measure or analyze the time taken for embedding and extraction, potentially using Big O notation.
  • Security Analysis (if applicable): Assess resistance to steganalysis or attacks against the encryption (for RDH-ED).
• Comparison Methods: List the existing RDH techniques you are comparing your method against.
• Implementation Details: Briefly mention the software/libraries used (e.g., MATLAB, Python with libraries like Open3D, PyMesh).

6. Results

Presenting Your Findings

• Quantitative Results: Present your results using tables and graphs.
  • Show embedding capacity vs. distortion (e.g., PSNR/HD) curves for your method and comparison methods across different models.
  • Tables summarizing average/maximum capacity, distortion, and computation time.
• Visual Results: Include figures showing the original model, the marked model (with embedded data), and potentially a difference map or zoomed-in views to illustrate the level of distortion.
• Analysis: Explain what the results mean. Highlight where your method outperforms others (e.g., "achieves 20% higher capacity at the same PSNR level compared to [Reference Method]").

Comparative Performance Analysis

Visualizing the trade-offs between different RDH methods across key metrics can be insightful. The radar chart below hypothetically compares different approaches. In your paper, you would populate this with your actual experimental results.

This chart helps visually compare the strengths and weaknesses of different approaches across multiple important criteria simultaneously.

Summarizing RDH Approaches

Different RDH techniques offer varying balances of capacity, distortion, and complexity. The table below provides a generalized comparison of common approaches. Your experimental results will provide specific values for your method and the ones you compare against.

Visualizing Concepts

Visual aids can significantly enhance understanding. While not directly about 3D mesh RDH, the following video discusses data hiding in encrypted images, touching upon concepts like encryption and embedding that are relevant in RDH-ED for 3D models.

This video provides a conceptual overview of data hiding within encrypted media, highlighting the general workflow involved, which shares similarities with RDH-ED techniques applied to 3D meshes.

Illustrative Example from Research

Research papers often include diagrams to illustrate their proposed methods. Below is an image from a study on crypto-space reversible data hiding for 3D mesh models, likely depicting a part of their algorithm or framework.

Source: Image associated with "Crypto-space reversible data hiding for 3D mesh models with k-means clustering based multi-group partition" via ScienceDirect/Elsevier.

Such diagrams help visualize complex processes like vertex partitioning, prediction strategies, or encryption/decryption steps involved in RDH schemes. Including clear, well-annotated diagrams in your own paper is highly recommended.

7. Discussion

Interpreting the Significance

• Interpret Results: Go beyond stating the numbers. Discuss *why* your method performs the way it does. Explain the trade-offs observed (e.g., "The adaptive grouping strategy leads to higher capacity in complex mesh regions but slightly increases computational overhead").
• Implications: Discuss the potential impact and applications of your research (e.g., suitability for secure medical imaging transmission, protecting intellectual property in CAD models, authenticating 3D printed objects).
• Limitations: Acknowledge the limitations of your work (e.g., "The method currently assumes manifold meshes," "Performance degrades on noisy scans," "Encryption overhead might be prohibitive for real-time applications"). Honesty builds credibility.
• Future Work: Suggest directions for future research based on your findings and limitations (e.g., extending the method to point clouds, improving robustness against geometric attacks, combining with other watermarking techniques, reducing computational complexity).

8. Conclusion (Paper Summary)

Summarizing Your Contribution

• Restate the problem and your main contributions concisely.
• Summarize the key advantages of your proposed method based on the results.
• Briefly reiterate the potential impact or applications. Avoid introducing new information here.

9. References

Acknowledging Sources

• List all cited works accurately, following the specific formatting guidelines of the conference (e.g., IEEE, ACM, Springer LNCS).
• Ensure every reference in the list is cited in the text, and vice-versa.

Frequently Asked Questions (FAQ)

References

• Reversible data hiding in encrypted 3D mesh models based on three-party secret sharing - ScienceDirect
• Adaptive Vertex Grouping Strategy for Reversible Data Hiding in Encrypted 3D Model - IEEE Xplore
• High-capacity reversible data hiding in encrypted 3D mesh models by reserving room in vertex coordinates - ScienceDirect
• Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-Group Partition and Closest Pair Prediction - MDPI
• MS-RDH: Multi-layer mesh synchronous reversible data hiding - SpringerLink
• Efficient Reversible Data Hiding for 3D Mesh Models Based on Multi-LSB Substitution and Ring-prediction - IEEE Xplore
• Homomorphic Encryption-Based Reversible Data Hiding for 3D Mesh Models - ResearchGate

Recommended Exploration

• How do different performance metrics like PSNR and Hausdorff Distance compare for evaluating 3D mesh RDH?
• What are the specific applications and challenges of using RDH in medical 3D models?
• What security vulnerabilities exist in RDH-ED schemes for 3D meshes and how can they be mitigated?
• How can reversible data hiding techniques be adapted for dynamic or animated 3D meshes?

Crafting Your Conference Paper: Reversible Data Hiding in 3D Mesh Models

A comprehensive guide to structuring and detailing your research for maximum impact.

Developing a conference paper on Reversible Data Hiding (RDH) in 3D mesh models requires a structured approach to effectively communicate your research contributions. RDH techniques allow embedding data into a 3D model such that the original, unaltered model can be perfectly recovered after data extraction. This guide outlines the essential sections and content needed for a compelling paper.

Key Highlights

• Balancing Act: Successfully implementing RDH in 3D meshes involves navigating the critical trade-off between maximizing embedding capacity (how much data can be hidden) and minimizing visual distortion to the model.
• Perfect Fidelity: The core promise of RDH is complete reversibility – ensuring the original 3D mesh can be restored without any loss or modification after the hidden data is extracted.
• Security in the Cloud: A significant area of RDH research focuses on encrypted 3D models (RDH-ED), allowing data hiding even when the model's content is protected, crucial for cloud storage and secure transmission.

Structuring Your Conference Paper

A well-structured paper guides the reader logically through your research. Here’s a standard format with detailed considerations for RDH in 3D meshes:

1. Title and Abstract

Crafting Your First Impression

• Title: Make it concise, informative, and reflective of your specific contribution (e.g., "High-Capacity Adaptive RDH for Encrypted 3D Point Clouds").
• Abstract: A brief summary (typically 150-250 words) covering the problem, your proposed method, key experiments, results (highlighting capacity and distortion metrics), and the significance of your work.

2. Introduction

Setting the Stage

• Context: Start with the growing importance and applications of 3D models (engineering, gaming, medicine, virtual/augmented reality, manufacturing).
• Introduce RDH: Define Reversible Data Hiding, emphasizing its ability to embed data losslessly. Explain why it's valuable (authentication, metadata embedding, covert communication, copyright protection).
• The Challenge with 3D Meshes: Discuss the unique difficulties of applying RDH to 3D meshes compared to 2D images (complex topology, irregular vertex distribution, sensitivity to geometric distortion).
• Problem Statement: Clearly articulate the specific problem your research addresses (e.g., low embedding capacity in existing methods, high distortion, lack of robustness, specific challenges in encrypted domains).
• Contribution & Objective: Briefly introduce your proposed method and state its main advantages and the objectives of your study (e.g., to develop a novel RDH scheme with higher capacity and lower distortion for encrypted 3D meshes using adaptive prediction).
• Paper Outline: Briefly mention the structure of the rest of your paper.

3. Literature Review / Related Work

Building on Existing Knowledge

• Survey Existing RDH Techniques: Discuss established RDH methods, categorizing them:
  • Spatial Domain: Methods directly modifying mesh geometry (vertex coordinates, connectivity). Examples include Histogram Shifting (HS), Difference Expansion (DE), Prediction Error Expansion (PEE), Least Significant Bit (LSB) substitution variations, Multi-Most Significant Bit (MSB) prediction.
  • Compressed Domain: Methods embedding data within compressed representations of the 3D mesh (e.g., manipulating quantized coefficients).
  • Encrypted Domain (RDH-ED): Techniques designed for embedding data into encrypted 3D models, often using homomorphic encryption or specific reservation strategies before encryption. Mention approaches like reserving room in LSBs before encryption or using techniques compatible with encryption schemes (e.g., Paillier cryptosystem).
• Analyze Limitations: Critically evaluate existing methods, pointing out their drawbacks regarding embedding capacity, visual distortion (measured by metrics like PSNR, Hausdorff Distance), computational complexity, security vulnerabilities, or applicability to specific mesh types or encrypted scenarios.
• Position Your Work: Clearly explain how your proposed method addresses these limitations and differs from previous approaches. Cite key papers relevant to your specific techniques (e.g., papers on adaptive vertex grouping, closest pair prediction, multi-layer embedding, specific encryption schemes).

Understanding RDH Techniques

Reversible data hiding techniques for 3D meshes can be broadly categorized based on the domain they operate in and the specific strategy used for embedding. The mindmap below illustrates some common approaches:

Your proposed method will likely fall into one or a combination of these categories. Clearly explaining the underlying principles of your chosen approach is crucial.

Presenting Your Proposed Method

This section is the core of your paper, detailing your unique contribution.

4. Methodology / Proposed Scheme

Detailing Your Innovation

• Overview: Provide a high-level description of your method and its workflow. A flowchart or diagram can be very effective here.
• Mesh Representation: Specify the 3D mesh format you work with (e.g., triangle mesh, point cloud) and how geometric/topological information (vertices, faces, edges, normals) is utilized.
• Preprocessing (if any): Describe any steps taken before embedding, such as mesh partitioning, vertex ordering, or encryption (for RDH-ED). If using encryption, detail the algorithm (e.g., Paillier homomorphic encryption, stream cipher).
• Data Embedding Process: This is the most critical part. Explain step-by-step:
  • How embedding locations are selected (e.g., based on vertex prediction errors, histogram bins, specific bit-planes).
  • The mechanism for creating space (e.g., shifting histogram bins, expanding prediction errors, modifying LSBs/MSBs). Describe any adaptive strategies (e.g., adaptive vertex grouping based on local complexity, adaptive prediction based on neighbor geometry).
  • How the secret data bits are inserted into the created space.
  • How auxiliary information (e.g., overflow/underflow location map, prediction parameters, original LSBs) needed for reversibility is handled (e.g., compressed using arithmetic coding, embedded alongside payload).
  Provide mathematical formulations or algorithms where appropriate. For example, if using PEE, show the prediction formula and the expansion function. Use MathJax for clear rendering: e.g., predictor \( \hat{v}_i = f(N(v_i)) \), prediction error \( e_i = v_i - \hat{v}_i \), and embedding \( e'_i = 2e_i + b \). \[ \text{Example Prediction Error Expansion:} \] \[ \hat{v}_i = \frac{1}{|N(v_i)|} \sum_{v_j \in N(v_i)} v_j \quad (\text{Predictor}) \] \[ e_i = v_i - \lfloor \hat{v}_i \rfloor \quad (\text{Integer Prediction Error}) \] \[ e'_i = \begin{cases} 2e_i + b & \text{if } e_i \in [T_n, T_p] \\ e_i + T_p + 1 & \text{if } e_i > T_p \\ e_i + T_n & \text{if } e_i < T_n \end{cases} \quad (\text{Expansion & Embedding bit } b) \]
• Data Extraction and Mesh Recovery Process: Clearly explain the inverse process:
  • How the receiver extracts the embedded data using the data hiding key.
  • How the auxiliary information is retrieved and used.
  • The exact steps to reverse the modifications and perfectly restore the original vertex coordinates or other modified attributes, proving the reversibility of your scheme.
• Security Considerations (especially for RDH-ED): Discuss how the confidentiality of the model (if encrypted) and the hidden data is maintained. Analyze resilience against potential attacks.

Example Algorithm Snippet

Including pseudo-code can clarify your algorithm:

# Simplified Python-like pseudo-code for RDH Embedding
def embed_data_rdh(mesh, secret_data_stream):
    vertices = mesh.get_vertices()
    aux_data = [] # To store info needed for reversal

    # 1. Classify vertices or select embedding locations
    embeddable_vertices = select_vertices_for_embedding(vertices)

    for vertex in embeddable_vertices:
        # 2. Predict vertex value (if using PEE)
        predicted_value = predict_vertex(vertex, mesh.get_neighbors(vertex))
        error = vertex.coordinate - predicted_value

        # 3. Check if error is expandable based on thresholds
        if is_expandable(error):
            # 4. Get next bit from secret data stream
            bit = secret_data_stream.get_next_bit()

            # 5. Modify error (e.g., expand) to embed bit
            new_error = expand_error(error, bit)
            vertex.coordinate = predicted_value + new_error

            # Store original LSB or other info if needed
            # aux_data.append(original_LSB_or_info) 
        else:
            # Handle non-expandable errors (e.g., shift)
            shifted_error = shift_error(error)
            vertex.coordinate = predicted_value + shifted_error

    # 6. Embed auxiliary data (e.g., location map, compressed info)
    embed_auxiliary_data(mesh, aux_data)

    return mesh # Return the modified mesh
  

Evaluating Performance

Robust evaluation is key to demonstrating the effectiveness of your RDH scheme. Comparisons against baseline or state-of-the-art methods are essential.

5. Experimental Setup

Ensuring Reproducibility

• Datasets: Specify the 3D models used for testing (e.g., Stanford Bunny, Dragon, Armadillo; source like repositories, specific datasets). Mention model characteristics (number of vertices/faces, complexity).
• Evaluation Metrics: Clearly define the metrics used to assess performance:
  • Embedding Capacity (EC): Total bits embedded (payload size), often measured in bits per vertex (bpv) or bits per face (bpf).
  • Distortion: Quantify the difference between the original and marked (data-embedded) mesh. Common metrics include:
    • Peak Signal-to-Noise Ratio (PSNR) applied to vertex coordinates. Higher PSNR means lower distortion. \( \text{PSNR} = 10 \log_{10} \left( \frac{\text{MAX}^2}{\text{MSE}} \right) \), where MAX is the maximum possible coordinate value range and MSE is the Mean Squared Error between original and marked vertex coordinates.
    • Hausdorff Distance (HD) measuring maximum geometric deviation. Lower HD is better.
    • Structural Similarity Index (SSIM) adapted for meshes, if applicable.
    • Visual inspection (include figures showing original vs. marked models, potentially highlighting differences).
  • Reversibility Check: Confirm that the restored mesh is identical to the original (e.g., bit-wise comparison of coordinates, MSE = 0).
  • Computational Complexity: Measure or analyze the time taken for embedding and extraction, potentially using Big O notation.
  • Security Analysis (if applicable): Assess resistance to steganalysis or attacks against the encryption (for RDH-ED).
• Comparison Methods: List the existing RDH techniques you are comparing your method against.
• Implementation Details: Briefly mention the software/libraries used (e.g., MATLAB, Python with libraries like Open3D, PyMesh).

6. Results

Presenting Your Findings

• Quantitative Results: Present your results using tables and graphs.
  • Show embedding capacity vs. distortion (e.g., PSNR/HD) curves for your method and comparison methods across different models.
  • Tables summarizing average/maximum capacity, distortion, and computation time.
• Visual Results: Include figures showing the original model, the marked model (with embedded data), and potentially a difference map or zoomed-in views to illustrate the level of distortion.
• Analysis: Explain what the results mean. Highlight where your method outperforms others (e.g., "achieves 20% higher capacity at the same PSNR level compared to [Reference Method]").

Comparative Performance Analysis

Visualizing the trade-offs between different RDH methods across key metrics can be insightful. The radar chart below hypothetically compares different approaches. In your paper, you would populate this with your actual experimental results.

This chart helps visually compare the strengths and weaknesses of different approaches across multiple important criteria simultaneously. Note that capacity data was scaled for visualization.

Summarizing RDH Approaches

Different RDH techniques offer varying balances of capacity, distortion, and complexity. The table below provides a generalized comparison of common approaches. Your experimental results will provide specific values for your method and the ones you compare against.

Visualizing Concepts

Visual aids can significantly enhance understanding. While not directly about 3D mesh RDH, the following video discusses data hiding in encrypted images, touching upon concepts like encryption and embedding that are relevant in RDH-ED for 3D models.

This video provides a conceptual overview of data hiding within encrypted media, highlighting the general workflow involved, which shares similarities with RDH-ED techniques applied to 3D meshes.

Illustrative Example from Research

Research papers often include diagrams to illustrate their proposed methods. Below is an image from a study on crypto-space reversible data hiding for 3D mesh models, likely depicting a part of their algorithm or framework.

Source: Image associated with "Crypto-space reversible data hiding for 3D mesh models with k-means clustering based multi-group partition" via ScienceDirect/Elsevier.

Such diagrams help visualize complex processes like vertex partitioning, prediction strategies, or encryption/decryption steps involved in RDH schemes. Including clear, well-annotated diagrams in your own paper is highly recommended.

7. Discussion

Interpreting the Significance

• Interpret Results: Go beyond stating the numbers. Discuss *why* your method performs the way it does. Explain the trade-offs observed (e.g., "The adaptive grouping strategy leads to higher capacity in complex mesh regions but slightly increases computational overhead").
• Implications: Discuss the potential impact and applications of your research (e.g., suitability for secure medical imaging transmission, protecting intellectual property in CAD models, authenticating 3D printed objects).
• Limitations: Acknowledge the limitations of your work (e.g., "The method currently assumes manifold meshes," "Performance degrades on noisy scans," "Encryption overhead might be prohibitive for real-time applications"). Honesty builds credibility.
• Future Work: Suggest directions for future research based on your findings and limitations (e.g., extending the method to point clouds, improving robustness against geometric attacks, combining with other watermarking techniques, reducing computational complexity).

8. Conclusion (Paper Summary)

Summarizing Your Contribution

• Restate the problem and your main contributions concisely.
• Summarize the key advantages of your proposed method based on the results.
• Briefly reiterate the potential impact or applications. Avoid introducing new information here.

9. References

Acknowledging Sources

• List all cited works accurately, following the specific formatting guidelines of the conference (e.g., IEEE, ACM, Springer LNCS).
• Ensure every reference in the list is cited in the text, and vice-versa.

Frequently Asked Questions (FAQ)

References

• Reversible data hiding in encrypted 3D mesh models based on three-party secret sharing - ScienceDirect
• Adaptive Vertex Grouping Strategy for Reversible Data Hiding in Encrypted 3D Model - IEEE Xplore
• High-capacity reversible data hiding in encrypted 3D mesh models by reserving room in vertex coordinates - ScienceDirect
• Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-Group Partition and Closest Pair Prediction - MDPI
• MS-RDH: Multi-layer mesh synchronous reversible data hiding - SpringerLink
• Efficient Reversible Data Hiding for 3D Mesh Models Based on Multi-LSB Substitution and Ring-prediction - IEEE Xplore
• Homomorphic Encryption-Based Reversible Data Hiding for 3D Mesh Models - ResearchGate

Recommended Exploration

How do different performance metrics like PSNR and Hausdorff Distance compare for evaluating 3D mesh RDH?

What are the specific applications and challenges of using RDH in medical 3D models?

What security vulnerabilities exist in RDH-ED schemes for 3D meshes and how can they be mitigated?

How can reversible data hiding techniques be adapted for dynamic or animated 3D meshes?

mdpi.com

A Novel Reversible Data Hiding Method for 3D Model in Homomorphic ...

mdpi.com

Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction

sciencedirect.com

Ring Co-XOR encryption based reversible data hiding for 3D mesh model ...

ieeexplore.ieee.org

Reversible Data Hiding Based on Bidirectional Histogram Modification ...

toptal.com

Using Embeddings to Make Complex Data Simple | Toptal®

github.com

GitHub - mikacuy/deformation_aware_embedding: Deformation-Aware ...

link.springer.com

A multi-layer mesh synchronized reversible data hiding algorithm on the 3D model | Multimedia Systems

timescale.com

A Beginner’s Guide to Vector Embeddings | Timescale

ieeexplore.ieee.org

Reversible Data Hiding for 3D Meshes in the PVQ-Compressed Domain | IEEE Conference Publication | IEEE Xplore

sciencedirect.com

High-capacity reversible data hiding in encrypted 3D mesh models based ...

ieeexplore.ieee.org

High Capacity Reversible Data Hiding in Encrypted 3D Mesh Models Based ...

arxiv.org

[2110.01010] High Capacity Reversible Data Hiding in Encrypted ...

encord.com

The Full Guide to Embeddings in Machine Learning | Encord

sciencedirect.com

Efficient separable reversible data hiding algorithm for compressed ...

researchgate.net

(PDF) Embedding data in 3D models

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High-capacity reversible data hiding in encrypted 3D mesh models ...

reddit.com

r/learnmachinelearning on Reddit: Info on embeddings for 3D models

ieeexplore.ieee.org

A reversible data hiding approach to mesh authentication | IEEE Conference Publication | IEEE Xplore

researchgate.net

Reversible watermark for 3D vertices based on data hiding in mesh ...

cookbook.openai.com

Visualizing embeddings in 3D | OpenAI Cookbook

ieeexplore.ieee.org

A Reversible Data Hiding Scheme for 3D Point Cloud Model | IEEE Conference Publication | IEEE Xplore

link.springer.com

Reversible data hiding for 3D mesh models with hybrid prediction and ...

researchgate.net

(PDF) Homomorphic Encryption-Based Reversible Data Hiding for 3D Mesh Models

developers.google.com

Embeddings: Embedding space and static embeddings | Machine Learning ...

ieeexplore.ieee.org

Reversible Data Hiding for Encrypted 3D Mesh Models With Secret Sharing ...

mdpi.com

Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-Group Partition and Closest Pair Prediction

arxiv.org

[2103.03764] A Convolutional Architecture for 3D Model Embedding

aws.amazon.com

What is Embedding? - Embeddings in Machine Learning Explained - AWS

ieeexplore.ieee.org

Efficient Reversible Data Hiding for 3D Mesh Models Based on Multi-LSB Substitution and Ring-prediction | IEEE Conference Publication | IEEE Xplore

researchgate.net

(PDF) Data Hiding on 3D Polygonal Meshes

github.com

RyanHangZhou/Reversible-Data-Hiding-Mesh - GitHub