An Optimized Spatial Data Hiding Scheme Combined with Convolutional Codes and Hilbert Scan
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
The upper and lower bounds of the information-hiding capacity of digital images
Information Sciences: an International Journal
A novel robust watermarking technique using IntDCT based AC prediction
WSEAS Transactions on Computers
WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Content adaptive watermark embedding in the multiwavelet transform using a stochastic image model
IWDW'02 Proceedings of the 1st international conference on Digital watermarking
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Estimating watermarking capacity in gray scale images based on image complexity
EURASIP Journal on Advances in Signal Processing
Tabu search based multi-watermarks embedding algorithm with multiple description coding
Information Sciences: an International Journal
The minimum detectable capacity of digital image information hiding
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
The maximum capacity and minimum detectable capacity of information hiding in digital images
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
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The evaluation of the number of bits that can be hidden within an image through digital watermarking is a crucial topic, which has been addressed only for additive watermarks. The evaluation of watermark capacity is very important because it allows to put a theoretical upper bound on the amount of information that can be hidden into an image by a given watermarking procedure, regardless of the watermark extraction technique. It is the purpose of this work to suggest a methodology for the evaluation of the watermark capacity in a nonadditive, non-Gaussian framework, and to discuss the results we obtained by applying it to a set of standard images