Abstract
In this paper, an efficient data hiding method that embeds data into absolute moment block truncation coding (AMBTC) codes is proposed. The AMBTC method represents image blocks by trios, and each trio consists of two quantization levels and an asymmetrically distributed bitmap. However, the asymmetric phenomena of bitmaps cause large degradation in image quality during data embedment. With the help of reference tables filled with symmetrical patterns, the proposed method exploits a symmetry adjustment model to modify the quantization levels in those smooth blocks to achieve the smallest distortion. If the block is complex, a lossless embedding method is performed to carry one additional bit. A sophisticated division switching mechanism is also proposed to modify a block from smooth to complex if the solution to the minimal distortion cannot be found. The payload can be adjusted by varying the threshold, or by embedding more bits into the quantization levels. The experiments indicate that the proposed work provides the best stego image quality under various payloads when comparing to the related prior works.
Highlights
Data hiding is a technique that embeds data into an innocent cover media for secret communication [1]
We proposed an efficient data hiding method dedicated to the absolute moment block truncation coding (AMBTC) compressed codes with the guidance of a reference table
We propose a novel data hiding method based on AMBTC compressed images
Summary
Data hiding is a technique that embeds data into an innocent cover media for secret communication [1]. The digital images are often used as the cover objects to convey messages because they are available and provide rich redundancies for data embedment. When images are chosen as the carriers, the images used for embedding messages are called cover images, while the embedded images are called stego images [2]. The hiding capacity, stego image quality, and un-detectability are the most important issues for a data hiding method in images. A large hiding capacity allows an image to carry more data, while a higher image quality means that the distortion of the stego image is smaller. The un-detectability allows the stego image to resist the statistical detection by the steganalyzers [3,4,5]
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