ACM Computing Surveys (CSUR)
Information Hiding Techniques for Steganography and Digital Watermarking
Information Hiding Techniques for Steganography and Digital Watermarking
Secure Steganographic Methods for Palette Images
IH '99 Proceedings of the Third International Workshop on Information Hiding
Hiding Information in Color Images Using Small Color Palettes
ISW '00 Proceedings of the Third International Workshop on Information Security
Luminance Quasi-Preserving Color Quantization for Digital Steganography to Palette-Based Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
An iterative method of palette-based image steganography
Pattern Recognition Letters
Steganographic Scheme for VQ Compressed Images Using Progressive Exponential Clustering
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Adaptive embedding techniques for VQ-compressed images
Information Sciences: an International Journal
An Efficient VQ-Based Data Hiding Scheme Using Voronoi Clustering
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 03
Analysis of parity assignment steganography in palette images
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
IH'05 Proceedings of the 7th international conference on Information Hiding
Multiclass Detector of Current Steganographic Methods for JPEG Format
IEEE Transactions on Information Forensics and Security
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Cluster indexing-based steganography is an important branch of data-hiding techniques. Such schemes normally achieve good balance between high embedding capacity and low embedding distortion. However, most cluster indexing-based steganographic schemes utilise less efficient clustering algorithms for embedding data, which causes redundancy and leaves room for increasing the embedding capacity further. In this paper, a new clustering algorithm, called progressive exponential clustering (PEC), is applied to increase the embedding capacity by avoiding redundancy. Meanwhile, a cluster expansion algorithm is also developed in order to further increase the capacity without sacrificing imperceptibility.