Entropy Based Data Hiding

Dear Colleagues,

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of an increased dissemination and distribution of multimedia content (text, audio, video, etc.) over the Internet, data hiding methods, such as digital watermarking and steganography, are becoming more and more important in providing multimedia security. Due to the complimentary nature of general requirements of these methods, i.e., imperceptibility, robustness, security and capacity, many data hiding schemes attempt to find optimal performance by applying concepts of information theory, such as entropy. Entropy has been used extensively to support data hiding algorithms. Examples include the use of information entropy as a “masking effect” of Just Noticeable Difference (JND) model in digital watermarking systems to obtain a better trade-off between imperceptibility and robustness; as a measure to evaluate the complexity of cover object to achieve Bit Error Rate (BER) constraint in steganographic schemes; as a criterion to determine the embedding positions in the cover data so as to cause minimal perceptual distortion; and as a measure to evaluate information leakage in the embedding process.

The goal of this Special Issue is to concentrate on (but not limited to) the improvement of data hiding algorithms through information entropy, and on the application of entropy in real-world data hiding techniques. It will bring together researchers and practitioners from different research fields including data hiding, signal processing, cryptography or information theory, among others, to contribute with original research outcomes that address issues in data hiding algorithms using information theory approaches.

Dr. David Megías Jiménez
Dr. Minoru Kuribayashi
Guest Editors

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