Recent Developments and Applications of Image Watermarking

Topic Information

Dear Colleagues,

The watermarking of multimedia products plays a vital role in copyright protection, authentication, and data security. Over the years, watermarking schemes have proven successful, especially in digital and network environments, often in conjunction with cryptography techniques. However, with the growing trend of printing watermarked images on physical media and capturing them using smartphones, watermarking also faces new challenges due to various attacks such as analog-to-digital transformations, signal and geometric distortions, and camera rotation.

The triple objective of watermarking—robustness, capacity, and imperceptibility—becomes exceptionally challenging in this context. To address these difficulties and explore new possibilities, researchers have been continually advancing traditional signal and pattern recognition techniques. Moreover, the emergence of deep learning technologies has shown great promise for pushing the boundaries of watermarking research.

We are pleased to announce this Topic, which is entitled “Recent Developments and Applications of Image Watermarking”, where we aim to bring together cutting-edge research and applications in this field. This Topic will serve as a platform for researchers and practitioners from various domains, including data hiding, signal processing, and cryptography, to share their original research contributions.

Topics of interest for this topic include, but are not limited to, the following:

• Novel Deep Learning Approaches for Watermarking: Exploring innovative deep learning techniques tailored to watermarking tasks to enhance robustness and imperceptibility.
• Deep Learning for Robust Watermark Detection and Extraction: Investigating methods to reliably detect and extract watermarks from multimedia content despite various attacks.
• Multimodal Watermarking with Deep Learning: Examining approaches that combine deep learning for watermarking in diverse types of media, such as images and audio.
• Deep Learning in Reversible Watermarking: Exploring techniques to achieve reversible watermarking, enabling the perfect recovery of the original content after extraction.
• Data Hiding with Generative Models: Investigating the application of generative models like GANs or VAEs for data hiding and robust watermarking.
• Transfer Learning for Watermarking: Studying the effectiveness of transfer learning in watermarking scenarios, where models trained on one dataset are fine-tuned for other domains.
• Robustness and Attacks: Evaluating the robustness of deep-learning-based watermarking schemes against various attacks, including adversarial and steganalysis attacks.
• Explainability in Watermarking with Deep Learning: Exploring methods to improve the interpretability and explainability of deep learning-based watermarking systems for legal and forensic applications.

We invite researchers and practitioners to submit their original research articles and case studies that shed light on these topics and contribute to the advancement of watermarking techniques.

Dr. Frederic Ros
Dr. Pedro M. B. Torres
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Cryptography cryptography	2.4	6.4	2017	19.9 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Entropy entropy	2.1	4.9	1999	20.9 Days	CHF 2600	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Journal of Cybersecurity and Privacy jcp	3.8	8.8	2021	23.7 Days	CHF 1200	Submit
Mathematics mathematics	2.3	5.4	2013	17.4 Days	CHF 2600	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (2 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Cryptography Electronics Mathematics Information Sci Entropy JCP

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

20 pages, 4278 KB  

Open AccessArticle

Image Watermarking Algorithm Leveraging Dual-Attention Synergy and Adaptive Multi-Scale Fusion

by Zhenghan Yang, Huadong Sun and Nuohan Lv

Electronics 2026, 15(12), 2580; https://doi.org/10.3390/electronics15122580 - 11 Jun 2026

Viewed by 302

Abstract

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale [...] Read more.

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale watermarking algorithm based on collaborative dual-attention mechanisms. The algorithm designs an adaptive multi-scale feature fusion module (MA-FFM) with a dynamic gating network in the encoder, which flexibly combines local multi-scale textures with global contextual information, overcoming the limitation of fixed fusion weights. In the decoder, a multi-level channel attention module is embedded to strengthen the extraction of watermark signals. The two attention modules work synergistically: the encoder focuses on adaptive feature fusion while the decoder leverages channel attention to selectively enhance watermark-related features, forming a dual-attention synergy that balances robustness and imperceptibility. Moreover, the dynamic gating network adaptively adjusts the contribution of local versus global features via learnable weights, whose evolution from approximately 0.51 to about 0.89 improves model interpretability. Experiments are conducted on the COCO 2017 dataset. Compared with HiDDeN, the proposed algorithm reduces the bit error rate (BER) from 0.1696 to 0.1538 under no attack with a relative reduction of 9.3%, increases PSNR by 0.61 dB, and improves SSIM from 0.9058 to 0.9077. Under various attacks—including JPEG compression, Gaussian noise, salt-and-pepper noise, and brightness/contrast adjustments—the BER remains consistently lower than that of HiDDeN. Ablation studies confirm the effectiveness of each module. Overall, the proposed algorithm preserves visual quality, improves the accuracy of watermark embedding and extraction, and exhibits strong generalization robustness against common image distortions. Full article

(This article belongs to the Topic Recent Developments and Applications of Image Watermarking)

►▼ Show Figures

Figure 1

26 pages, 3266 KB  

Open AccessArticle

High-Capacity Dual-Image Reversible Data Hiding in AMBTC Using Difference Expansion with Block-Wise HMAC Authentication

by Cheonshik Kim, Ching-Nung Yang and Lu Leng

Appl. Sci. 2026, 16(6), 2815; https://doi.org/10.3390/app16062815 - 15 Mar 2026

Cited by 1 | Viewed by 374

Abstract

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based [...] Read more.

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

(This article belongs to the Topic Recent Developments and Applications of Image Watermarking)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-2