Multimedia Steganography and Watermarking in the AI Era: Methods, Robustness and Applications
A special issue of Applied Sciences (ISSN 2076-3417).
Deadline for manuscript submissions: 30 March 2026 | Viewed by 59
Special Issue Editors
Interests: computer vision; image processing and analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals
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,
As multimedia technologies continue to rapidly evolve, the fields of steganography and watermarking are expanding in both technical depth and their breadth of applications. From copyright protection and tamper detection to covert communication and digital forensics, there is a growing demand for secure, adaptive, and explainable techniques across visual, audio, textual, and multimodal content.
With the rise of artificial intelligence—particularly deep learning, generative models, and representation learning—researchers are rethinking the foundations of multimedia steganography and watermarking. At the same time, traditional signal processing, frequency-domain embedding, and cryptographic approaches remain vital to achieving interpretability, controllability, and robustness in real-world settings.
This Special Issue will capture the state of the art in multimedia steganography and watermarking in the AI era, with balanced emphases on theory, models, robustness, and applications. We welcome contributions focusing on both AI-powered and conventional approaches that advance security, resilience, and effectiveness in multimedia steganography, watermarking, and data hiding.
Topics of interest include but are not limited to the following:
- AI-based steganography and watermarking (e.g., utilizing GANs, transformers, CLIP, or diffusion models);
- Representation learning for robust watermarking;
- Multimodal and cross-domain data hiding (e.g., involving text–image, audio–video, or image–video data);
- Steganalysis, adversarial attacks, and defense mechanisms;
- Traditional and hybrid embedding techniques (DCT/DWT, SVD, etc.);
- Data hiding in compressed or encrypted multimedia streams;
- Real-world applications in forensics, content authentication, and secure communications;
- Benchmarking datasets, evaluation protocols, and explainability.
We especially encourage submissions that bridge AI innovation with real-world constraints, propose robust models, or present new challenges and benchmarks in multimedia data hiding.
We look forward to your contributions to this innovative and relevant Special Issue.
Dr. Xin Zhong
Prof. Dr. Frank Y. Shih
Guest Editors
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Keywords
- watermarking
- steganography
- data hiding
- deep learning-based
- representation learning
- multimodality
- robustness and defense
- generative model-based
- multimedia security
- multimedia coding and encryption
- steganalysis
- real-world applications
- explainable AI
- breakthroughs from traditional methods
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