We propose a new approach to steganography for information hiding in digital media. We refer to it as blind consistency-based steganography (BCBS). Our main concerns in designing BCBS is high imperceptibility, high security level, and large capacity. Steganography differs from digital watermarking because both the information and the very existence of the information are hidden. Even though the discussion focuses on digital images, BCBS can be applied to any digital media. While existing steganographic approaches all have different advantages for specific applications, they also suffer a similar problem: such systems are unable to handle subterfuge attacks, i.e., they cannot deal with the opponents who not only detect a message, but also render it useless, or even worse, modify it to the opponent's favor. The breakthrough of BCBS is that it not only decodes the message exactly, it also detects if the message has been tampered without using any extra error correction. The detection is integrated into the process of decoding. Another advantage of BCBS is the "blindness" of the decoding process, meaning the decoding can be operated without access to the cover image. The encoding and decoding process are detailed in the paper, along with the analysis on imperceptibility, security, and capacity. Experimental results are provided as well.

Square and hexagonal spatial samplings, because of their processing ease, are used most widely in image and signal processing. The authors develop mathematical tools for estimating quantization error in hexagonal sensory configurations. These include analytic expressions for the average error and the error distribution of a function of an arbitrarily large number of hexagonally quantized variables. The two quantities (the average error and the error distribution) are essential in assessing the reliability of a given algorithm. The corresponding expressions for square spatial sampling are presented for comparison; they can be used to determine which sampling technique would result in less quantization error for a particular algorithm. Such a comparison is important due to the paramount role that quantization error plays in computational approaches to computer vision. Some general observations in regard to the relative accuracy of hexagonal vs. square quantization are also presented.>