Multiple Bit Planes Research Articles

Secret data sharing through coverless video steganography based on bit plane segmentation

Recently information hiding is acquiring significance with the rising multimedia content over the web. Coverless steganography has been perceived as quite possibly the most solid way to deal with moving classified information and has turned into a research problem area. Considering video as a transporter, coverless steganography is expanding prominently because of its greater number of features than other transporters like text and images. Usually, the existing scheme hides confidential information based on single-frame features in the video. A secret data sharing through coverless video steganography technique based on bit-plane segmentation has been proposed. After extracting frames from the video, each frame is split into multiple bit-planes using BPCS(Bit-plane complexity segmentation). The hash sequences are obtained from these bit-planes by calculating the mean values of corresponding bit-plane sub-blocks. A retrieval database is constructed, including the relation between obtained hash sequences and bit-plane features. To begin with, the sender altered the secret data into a piece stream and divided it into equivalent lengths. The related retrieval information matching the fragmented secret information is transmitted to the recipient over the insecure channel. The experimental results show that the proposed technique achieves better robustness to various attacks, larger capacity, less time cost for extracting hash sequences, and a higher concealing success rate than existing coverless video steganography techniques.

Parallel image encryption with bitplane decomposition and genetic algorithm

Image encryption is an efficient technique to protect image content from unauthorized parties. In this paper a parallel image encryption method based on bitplane decomposition is proposed. The original grayscale image is converted to a set of binary images by local binary pattern (LBP) technique and bitplane decomposition (BPD) methods. Then, permutation and substitution steps are performed by genetic algorithm (GA) using crossover and mutation operations. Finally, these scrambled bitplanes are combined together to obtain encrypted image. Instead of random population selection in GA, a deterministic method with security keys is utilized to improve security level. The proposed encryption method has parallel processing capability for multiple bitplanes encryption. This distributed GA with multiple populations increases encryption speed and makes it suitable for real-time applications. Simulations and security analysis are done to demonstrate efficiency of our algorithm.

Wireless Holographic Image Communications Relying on Unequal Error Protected Bitplanes

Holography is considered to be one of the most promising techniques of goggle-free visualization for the near-future. We consider wireless transmission of digital holograms, which are partitioned into multiple bitplanes that are then independently encoded by a forward error correction (FEC) code for transmission over wireless channels. The coding rates of these bitplanes will be optimized at the transmitter for the sake of achieving an improved holographic peak signal-to-noise ratio (PSNR) at the receiver. Our simulation results show that up to 2.6 dB of $E_{b}/N_{0}$ or 12.5 dB of PSNR improvements may be achieved when employing a recursive systematic convolutional code.

High Capacity Image Steganography Using Adjunctive Numerical Representations With Multiple Bit-Plane Decomposition Methods

LSB steganography is a one of the most widely used methods for implementing covert data channels in image file exchanges [1][2]. The low computational complexity and implementation simplicity of the algorithm are significant factors for its popularity with the primary reason being low image distortion. Many attempts have been made to increase the embedding capacity of LSB algorithms by expanding into the second or third binary layers of the image while maintaining a low probability of detection with minimal distortive effects [2][3][4]. In this paper, we introduce an advanced technique for covertly embedding data within images using redundant number system decomposition over non-standard digital bit planes. Both grayscale and bit-mapped images are equally effective as cover files. It will be shown that this unique steganography method has minimal visual distortive affects while also preserving the cover file statistics, making it less susceptible to most general steganography detection algorithms.

High Capacity Image Steganography Using Adjunctive Numerical Representations With Multiple Bit-Plane Decomposition Methods

LSB steganography is a one of the most widely used methods for implementing covert data channels in image file exchanges [1][2]. The low computational complexity and implementation simplicity of the algorithm are significant factors for its popularity with the primary reason being low image distortion. Many attempts have been made to increase the embedding capacity of LSB algorithms by expanding into the second or third binary layers of the image while maintaining a low probability of detection with minimal distortive effects [2][3][4]. In this paper, we introduce an advanced technique for covertly embedding data within images using redundant number system decomposition over non-standard digital bit planes. Both grayscale and bit-mapped images are equally effective as cover files. It will be shown that this unique steganography method has minimal visual distortive affects while also preserving the cover file statistics, making it less susceptible to most general steganography detection algorithms.

Graphics processing unit-accelerated joint-bitplane belief propagation algorithm in DSC

The M-ary source with nonstationary correlation can be encoded with a single binary low-density parity-check (LDPC) code and decoded together in distributed source coding. The joint-bitplane belief propagation (JBBP) is a useful decoding algorithm for multiple bitplanes of an M-ary source. However, it suffers from the drawbacks of low computational efficiency and long execution time. Motivated by the evolution of the Graphics Processing Unit (GPU) and the inherent parallel characteristic of the JBBP, we propose a novel approach for the computationally intensive processing of the JBBP algorithm on GPU using the compute unified device architecture programming model. Two different parallel modes are utilized for the belief passing between different nodes of the JBBP. It is found that the bottlenecks of the JBBP lie in computing the overall probability mass functions (pmfs) of symbol nodes and the overall beliefs of bit nodes. Thus, a data partitioning method is leveraged to split a large array of pmfs into small pieces which can be loaded into L1 cache instead of global memory. The optimal block size is selected which not only assigns as large L1 cache as possible for individual thread, but also guarantees multiple active warps in each stream multiprocessor. Experimental results show that when the length-6336 (length-50,688, resp.) LDPC accumulate (LDPCA) code is used to compress the source, the JBBP decoder can achieve about 20$$\times $$× (41$$\times $$×, resp.) speedup on GPU compared with the original C code on CPU. Better performance would be further obtained with longer LDPCA codes. Moreover, the parallel JBBP is also applied in hyperspectral image compression and video coding and it shows good speedup performance.

Asymmetric Slepian-Wolf Coding of Nonstationarily-Correlated M-ary Sources with Sliding-Window Belief Propagation

Asymmetric Slepian-Wolf coding (ASWC) of M-ary sources with nonstationary correlation is a very useful model for many practical problems. An effective implementation of this coding scheme is to binarize each M-ary source into multiple bitplanes which are then compressed by a single binary low-density parity-check (LDPC) code. Though the inter-bitplane correlation of M-ary sources can be exploited at the decoder by the joint-bitplane belief propagation (JBBP) algorithm, accurate online estimation of varying local source correlation still remains a major challenge. To tackle this problem, this paper proposes an M-ary counterpart of the sliding-window belief propagation (SWBP) algorithm to realize simultaneous source recovery and correlation estimation. To search for the optimal sliding-window size, the expected rate is raised as a new criterion. Moreover, an adaptive method is proposed to decide whether correlation reestimation is necessary. The M-ary SWBP (MSWBP) algorithm is then generalized to obtain its 2D form, which can be used to tackle the ASWC of 2D M-ary sources with nonstationary correlation. The developed 1D/2D-MSWBP algorithm inherits all merits of the original binary SWBP algorithm, e.g., near-optimal coding efficiency, low complexity, insensitivity to initial settings, etc., making it a very attractive technique in practice.

Steganalysis of adaptive image steganography in multiple gray code bit-planes

For detecting the MBPIS (multiple bit-planes image steganography), which embeds the messages into the multiple Gray code bit-planes, a steganalysis method is proposed to estimate the embedding ratios based on sample pairs analysis. The proposed method combines appropriate trace sets of a more elegant and pellucid sample pair analysis model to estimate the modification ratio of each natural binary bit-plane. Then the modification ratios in Gray code bit-planes are estimated from the modification ratios in natural binary bit-planes based on the relationship between natural binary bit-planes and Gray code bit-planes. Finally, considering the matrix encoding in MBPIS, the embedding ratios are obtained from the modification ratios in Gray code bit-planes. Experimental results show that compared with existing steganalysis methods, the proposed method can not only detect the stego images of MBPIS more reliably, but also estimate the embedding ratios of MBPIS with smaller errors. When the embedding ratio in the first Gray code bit-plane is not less than 0.1, the average absolute error of the proposed method will less than it of the ID3SPA by 0.04 at least.

Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve

Quantitative histomorphometry is the current gold standard for objective measurement of nerve architecture and its components. Many methods still in use rely heavily upon manual techniques that are prohibitively time consuming, predisposing to operator fatigue, sampling error, and overall limited reproducibility. More recently, investigators have attempted to combine the speed of automated morphometry with the accuracy of manual and semi-automated methods. Systematic refinements in binary imaging analysis techniques combined with an algorithmic approach allow for more exhaustive characterization of nerve parameters in the surgically relevant injury paradigms of regeneration following crush, transection, and nerve gap injuries. The binary imaging method introduced here uses multiple bitplanes to achieve reproducible, high throughput quantitative assessment of peripheral nerve. Number of myelinated axons, myelinated fiber diameter, myelin thickness, fiber distributions, myelinated fiber density, and neural debris can be quantitatively evaluated with stratification of raw data by nerve component. Results of this semi-automated method are validated by comparing values against those obtained with manual techniques. The use of this approach results in more rapid, accurate, and complete assessment of myelinated axons than manual techniques.