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Applied Sciences
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Recent Advances in Multimedia Steganography and Watermarking
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Recent Advances in Multimedia Steganography and Watermarking

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 9238

Special Issue Editors

Dr. Xin Zhong

E-Mail Website
Guest Editor
Department of Computer Science, College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Interests: computer vision; image processing and analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Frank Y. Shih

E-Mail Website
Guest Editor
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
Interests: artificial intelligence; deep learning; image processing; watermarking and steganography; digital forensics; pattern recognition; bioinformatics; biomedical engineering; fuzzy logic; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Being used in increasingly diverse multimedia application scenarios (e.g., covert communication, authentication, and security for images, audio, and texts), modern steganography and watermarking methods require the exploration of adaptive and dynamic solutions. This Special Issue invites original work to report the latest advances in steganography and watermarking, including but not limited to theory, algorithm, application, design, implementation, and case study. Works integrating emerging methods (e.g., artificial intelligence and machine learning) to address current difficulties in various scenarios are particularly welcome. Other closely related areas and interdisciplinary studies (e.g., data hiding, data communication, and multimedia coding/encryption) will also be prioritized. The participation of leading researchers will open up possibilities for exploring the related and important extensions of this area.

Dr. Xin Zhong
Prof. Dr. Frank Y. Shih
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • steganography
  • watermarking
  • data hiding
  • emerging methods
  • multimedia coding/encryption
  • steganalysis

Published Papers (5 papers)

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Research

18 pages, 4449 KiB  
Article
ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images
by Can Li, Hua Sun, Changhong Wang, Sheng Chen, Xi Liu, Yi Zhang, Na Ren and Deyu Tong
Appl. Sci. 2024, 14(1), 435; https://doi.org/10.3390/app14010435 - 03 Jan 2024
Viewed by 815
Abstract
In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end [...] Read more.
In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Steganography and Watermarking)
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15 pages, 2247 KiB  
Article
Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
by Ammar Odeh, Anas Abu Taleb, Tareq Alhajahjeh and Francisco Navarro
Appl. Sci. 2023, 13(24), 13291; https://doi.org/10.3390/app132413291 - 15 Dec 2023
Viewed by 761
Abstract
Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly [...] Read more.
Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Steganography and Watermarking)
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22 pages, 5998 KiB  
Article
Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
by Yaqin Jiao, Cong Ma, Juhua Luo and Yinguo Qiu
Appl. Sci. 2023, 13(24), 13088; https://doi.org/10.3390/app132413088 - 07 Dec 2023
Cited by 1 | Viewed by 3736
Abstract
To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered [...] Read more.
To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Steganography and Watermarking)
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22 pages, 9003 KiB  
Article
Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters
by Qianwen Zhou, Changqing Zhu and Na Ren
Appl. Sci. 2023, 13(23), 12957; https://doi.org/10.3390/app132312957 - 04 Dec 2023
Viewed by 1035
Abstract
With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM [...] Read more.
With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Steganography and Watermarking)
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21 pages, 2320 KiB  
Article
A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
by Xin Zhong, Arjon Das, Fahad Alrasheedi and Abdullah Tanvir
Appl. Sci. 2023, 13(21), 11852; https://doi.org/10.3390/app132111852 - 30 Oct 2023
Cited by 2 | Viewed by 1973
Abstract
This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, [...] Read more.
This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Steganography and Watermarking)
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