Computational Methods and Algorithms for Multimedia Data Analysis and Security

Dear Colleagues,

In recent years, the proliferation of multimedia data across various platforms has led to an urgent need for effective analysis algorithms and robust data security measures. Multimedia data, including images, videos, and audio files, present unique challenges due to their diverse formats, large volumes, and complex content. Furthermore, ensuring the confidentiality, integrity, and availability of multimedia data is crucial for protecting sensitive information and maintaining user privacy. This Special Issue aims to showcase cutting-edge research in the fields of multimedia computing, data security algorithms, and optimization strategies, highlighting innovative algorithms, methodologies, and solutions to address these challenges. Within this context, the topics covered in the Special Issue encompass diverse deep learning models incorporating blockchain or repurposing. These models span various applications, including transfer learning, meta-learning, continuous learning, model fine-tuning, model retraining, model reuse, representation learning, blockchain technology, federated learning, etc.

We look forward to receiving your interesting submissions.

Topics include, but are not limited to, the following:

• Advanced multimedia content computing and understanding techniques;
• Deep learning and artificial intelligence for multimedia data processing;
• Multimedia feature extraction, representation, and classification;
• Multimedia retrieval algorithm;
• Privacy-preserving multimedia processing and sharing;
• Multimedia authentication and integrity verification;
• Secure multimedia communication and streaming protocols;
• Multimedia data protection in cloud computing and edge computing environments;
• Person re-identification;
• Blockchain theory and application;
• Blockchain regulation and technology.

Dr. Keyang Cheng
Prof. Dr. Liangmin Wang
Guest Editors

Manuscript Submission Information

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• multimedia computing
• transfer learning
• meta learning
• continuous learning
• model fine-tuning
• model retraining
• model reuse
• representation learning
• blockchain
• federated learning