KEYWORDS: Mobile devices, Steganography, Computer programming, Steganalysis, Data hiding, Image processing, Image resolution, Computer security, Information security, Binary data
Adaptive steganography, an intelligent approach to message hiding, integrated with matrix encoding and pn-sequences serves as a promising resolution to recent security assurance concerns. Incorporating the above data hiding concepts with established cryptographic protocols in wireless communication would greatly increase the security and privacy of transmitting sensitive information. We present an algorithm which will address the following problems: 1) low embedding capacity in mobile devices due to fixed image dimensions and memory constraints, 2) compatibility between mobile and land based desktop computers, and 3) detection of stego images by widely available steganalysis software [1-3]. Consistent with the smaller available memory, processor capabilities, and limited resolution associated with mobile devices, we propose a more magnified approach to steganography by focusing adaptive efforts at the pixel level. This deeper method, in comparison to the block processing techniques commonly found in existing adaptive methods, allows an increase in capacity while still offering a desired level of security. Based on computer simulations using high resolution, natural imagery and mobile device captured images, comparisons show that the proposed method securely allows an increased amount of embedding capacity but still avoids detection by varying steganalysis techniques.
KEYWORDS: Image processing, Steganography, Data hiding, Statistical analysis, Digital imaging, Steganalysis, Digital watermarking, Image analysis, Computer simulations, Binary data
Adaptive steganography is a statistical approach for hiding the digital information into another form of digital media. The goal is to ensure the changes introduced into the cover image remain consistent with the natural noise model associated with digital images. There are generally two classes of steganography − global and local. The global class encompasses all non-adaptive techniques and is the simplest to apply and easiest to detect. The second classification is the local class, which defines most of the present adaptive techniques. We propose a new adaptive technique that is able to overcome embedding capacity limitations and reduce the revealing artifacts that are customarily introduced when applying other embedding methods. To obtain the objectives, we introduce a third faction which is the pixel focused class of steganography. Applying a new adaptive T-order statistical local characterization, the proposed algorithm is able to adaptively select the number of bits to embed per pixel. Additionally, a histogram retention process, an evaluation measure based on the cover image and statistical analysis allow for the embedding of information in a manner which ensures soundness from multiple statistical aspects. Based on the results of simulated experiments, our method is shown to securely allow an increased amount of embedding capacity, simultaneously avoiding detection by varying steganalysis techniques.
Adaptive steganographic techniques have become a standard direction taken when striving to complicate the detection of secret communication. The consideration of cover image features when embedding information is an effort to insert digital media while keeping the visual and the statistical properties of the cover image intact. There are several such embedding methods in existence today, applicable for different formats of images and with contrasting approaches. In this paper, we propose a new adaptive embedding technique which alters the least significant bit layers of an image. This technique is driven by three separate functions: (1) Adaptive selection of locations to embed. (2) Adaptive selection of number of bits per pixel to embed. (3) Adaptive selection of manner in which the information is inserted. Through the application of sensitive median-based statistical estimation and a recorded account of actions taken, the proposed algorithms are able to provide the desired level of security, both visually and statistically. In comparison with other methods offering the same level of security, the new technique is able to offer a greater embedding capacity. In addition, for the sake of thorough investigation and fair comparison, we will introduce a new stego capacity measure which will offer a means of comparing steganography methods applied across different formats of images. Finally, this new algorithm is created with the intention of implementing a new visual communication system acknowledging low energy consumption and limited computationally related resources.
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