Bit Planes Of Image Research Articles

Encrypted image processing using compression and reversible data hiding

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.

Image encryption algorithm based on 2D hyper-chaotic system and central dogma of molecular biology

Abstract With the widespread use of images, image security has received much attention. Image security can be guaranteed by encrypting the plain image and transmitting the corresponding cipher image. This paper proposes an image encryption algorithm based on the novel two-dimensional (2D) hyper-chaotic system, bidirectional diagonal crossover transformations (BDCTs) and central dogma of molecular biology. Firstly, six chaotic sequences are generated using the proposed hyper-chaotic system, which are used in the permutation and diffusion processes. Secondly, the 5th–8th bit-planes of plain image are permuted using the designed BDCTs. Thirdly, the permuted image is dynamically encoded into a quaternary DNA sequence, and then the quaternary DNA sequence is transcribed into a quaternary RNA sequence. After that, the quaternary RNA sequence is diffused using quaternary RNA operations. Finally, the diffused RNA sequence is translated into a codon sequence, and then the designed codon-level multipoint crossover scheme is used to further improve the diffusion effect. Experiment results and security analyses demonstrate that our algorithm has high security and efficiency. In other words, our algorithm is quite suitable for real-time image cryptosystems.

Separate and Combined Effective Coding of Bit Planes of Grayscale Images

Currently, an approach involving a coder with a combined structure for compressing images combining several different coders, the system for connecting them to various bit planes, and the control system for these connections have not been studied. Thus, there is a need to develop a structure and study the effectiveness of a combined codec for compressing images of various types without loss in the spatial domain based on arithmetic and (Run-Length Encoding) RLE-coding algorithms. The essence of separate effective coding is to use independent coders of the same type or one coder connected to the planes alternately in order to compress the higher and lower bit planes of the image or their combinations. In this paper, the results of studying the effectiveness of using a combination of arithmetic and RLE coding for several types of images are presented. As a result of developing this structure, the effectiveness of combined coding for compressing the differences in the channels of hyperspectral images (HSI) has been established, as hyperspectral images consist of multi-spectral bands, instead of just the typical three bands (RGB) or (YCbCr) found in regular images. Where, each pixel in a hyperspectral image represents the entire spectrum of light reflected by the object or scene at that particular location.

Robustness and Explainability of Image Classification Based on QCNN

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.

AN EFFICIENT ALGORITHMIC SOLUTION FOR HIGH PAYLOAD GOOD IMPERCEPTIBILITY HYBRID IMAGE STEGANOGRAPHY


 
 
 A hybrid steganographic technique has been proposed to enlarge the size of secret message and to hide several images in one cover image. Initially, the secret images were compressed using JPEG 2000 before embedding them to the cover image. Second least significant bit plane of cover image was entirely replaced by secret message while tri-ways-pixel-value-differencing (TPVD) was used to hide secret message in remaining seven-bit cover image. The quality of stego image was enhanced by residual image coding (RIC). The cover image was divided in four sub-images, three of them provided cover for secret message using proposed technique while fourth sub-image was used to embed RIC of stego image using only TPVD, to ensure better quality in terms of peak signal to noise ratio (PSNR). Proposed technique provided the opportunity to transmit secret data without detection by different steganalysis techniques like dual statistics steganalysis. Experimental results verified that proposed technique achieved high payload with better image quality than TPVD scheme.
 
 

A Novel Image Encryption Algorithm Based on 2D Self-Coupling Sine Map

Since the current general image encryption algorithms based on chaotic systems and classical permutation diffusion structures typically suffer from stochastic degradation, simple algorithm structures, and reduced execution efficiency when dealing with images with large data, this paper proposes a new multiprocess image encryption algorithm based on a new hyperchaotic coupled sine map. To begin with, by performing dynamic analyses of the map, including equilibrium points, bifurcation diagrams, Lyapunov exponent diagrams, and phase diagrams, associated performance analyses show high spectral entropy (SE) values and permutation entropy (PE) values. Meanwhile, sequences generated by the proposed map are able to pass the NIST randomness test, which shows its suitability for the design of encryption algorithms. Then, a chaos-based image encryption algorithm is proposed in this paper: In the preprocessing stage, the images are sparsely sampled using a compressed-sensing method; in the permutation stage, the image is decomposed into eight bit-plane images containing only 0s and 1s and a clockwise cyclic shift operation based on a chaotic sequence is applied in each bit-plane image to achieve a bit-level permutation effect; in the diffusion stage, the block DNA random computing is applied to the permuted image to modify the pixel values and the final cipher image is obtained. In addition, the initial value of the map is determined by the preset initial value, user-defined strings, and the Hash value of the plain image, where the Hash value of the plain image is calculated by the SHA-256 algorithm. Finally, the analysis of the algorithm demonstrates that the algorithm exhibits extremely strong plaintext correlation externally and excellent encryption performance in terms of attack resistance and execution efficiency.

Defending against Adversarial Attacks in Deep Learning with Robust Auxiliary Classifiers Utilizing Bit-plane Slicing

Deep Neural Networks (DNNs) have been widely used in variety of fields with great success. However, recent research indicates that DNNs are susceptible to adversarial attacks, which can easily fool the well-trained DNN-based classifiers without being detected by human eyes. In this article, we propose to integrate the target DNN model with our robust bit-plane classifiers to defend against adversarial attacks. The bit-plane classifiers take bit-planes of input images for convolution, which is motivated by our observation that successful attacks aim to generate imperceptible perturbations, and they mainly affect the low-order bits of pixels in clean images when adding the perturbations. We also propose two metrics, bit-plane perturbation rate and channel modification rate, to further explain the robustness of bit-plane classifiers. We discuss potential adaptive attack and find that our defense can be effective as long as the adversarial examples are qualified. We conduct experiments on dataset CIFAR-10 and GTSRB under white-box attack and black-box attack. The results show that our defense method can effectively increase the average model accuracy from 16.23% to 83.53% under white-box attack and from 40.65% to 88.14% under black-box attack on CIFAR-10 without sacrificing the accuracy of clean images.

Image Compression Software Design for Zhurong Mars Exploration Rover

Because of the limited number of information transmission channels and high bit error rate in Mars image transmission, a method of image compression software design and realization of Mars rover was proposed. The characteristics of rover image data management were analyzed, and the three capabilities that the rover image compression software needed to have were concluded: uniform file management capability, high-efficiency compression capability, and high fault tolerance capability. For uniform file management capability, a file system for uniform management of multiple types of image data was proposed. To meet the high efficiency and high fault tolerance requirements of the compression algorithm, based on run-length coding algorithm of first-1 bit, the three important processes of multi-load image unification processing, color image preprocessing, and segmented bit-plane image coding were expanded, to improve compression performance and fault tolerance. Then a complete set of image compression software design schemes was formed, using FPGA+DSP, and the complete set of software was realized. Finally a simulation verification system was used to verify the compression effect. Verification results show the effectiveness of the algorithm. This software has been verified by China’s Zhurong Mars rover and can effectively meet the needs of Mars exploration missions. The software design can be used as a reference for image compression in subsequent ultra-long-distance deep space explorations.

Reversible Data Hiding With Hierarchical Embedding for Encrypted Images

Reversible data hiding in encrypted images (RDHEI) is an effective technique of data security. Most state-of-the-art RDHEI methods do not achieve desirable payload yet. To address this problem, we propose a new RDHEI method with hierarchical embedding. Our contributions are twofold. (1) A novel technique of hierarchical label map generation is proposed for the bit-planes of plaintext image. The hierarchical label map is calculated by using prediction technique, and it is compressed and embedded into the encrypted image. (2) Hierarchical embedding is designed to achieve a high embedding payload. This embedding technique hierarchically divides prediction errors into three kinds: small-magnitude, medium-magnitude, and large-magnitude, which are marked by different labels. Different from the conventional techniques, pixels with small-magnitude/large-magnitude prediction errors are both used to accommodate secret bits in the hierarchical embedding technique, and therefore contribute a high embedding payload. Experiments on two standard datasets are discussed to validate the proposed RDHEI method. The results demonstrate that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average payloads of the proposed RDHEI method are 3.4568 bpp and 3.6823 bpp for BOWS-2 dataset and BOSSbase dataset, respectively.

Defect detection methods using boolean functions and the [formula omitted]-coefficient between bit-plane slices

We establish two automated defect detection (ADD) algorithms and apply them to detect faults in printed circuit boards (PCBs). Both techniques are referential and are implemented on the binary bit-plane images of the PCBs. In the first algorithm we measure the association between reference and inspected images via the φ-correlation coefficient of percentage statistics, while in the second approach we apply a carefully selected boolean function, as well as a smoothing median filter. Both techniques show a high accuracy, which is comparable to the state-of-the-art techniques, but in a notably faster time. For instance, the boolean function approach is faster than the use of the normalized cross correlation (NCC) by 1700% and it is faster than the use of a fast form of the NCC by 700%, while the use of the φ-correlation coefficient accelerates the procedure by 500% and 200% for the use of the NCC and some of its fast forms respectively.

A Chaotic Image Encryption Scheme Based on Hénon–Chebyshev Modulation Map and Genetic Operations

Thanks to the complex characteristics of ergodicity, pseudo-randomness and sensitivity in initial conditions, chaotic systems have been widely applied in the field of cryptography. By cascading the Hénon map and the Chebyshev map, a new two-dimensional Hénon–Chebyshev modulation map (2D-HCMM) is proposed in this paper. Several methods of objective assessment, including phase diagrams, bifurcation diagrams, Lyapunov exponents and information entropy, are utilized to analyze the dynamics of the 2D-HCMM. The results show that the 2D-HCMM possesses better ergodicity and unpredictability, with larger chaotic ranges, compared with the original chaotic maps. By using the proposed map and the essential principles of genetic recombination and genetic mutation, a new image encryption scheme is proposed. In this scheme, the bit planes of image are substituted by genetic recombination operation, and the pixel values are scrambled randomly by genetic mutation operation. The simulation results and security analysis demonstrate that the proposed scheme not only can resist various conventional attacks, but also possesses a fast speed, achieving a good trade-off between security and efficiency.

Complexity Scalable Bitplane Image Coding With Parallel Coefficient Processing

Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed method processes the coefficients in parallel, suiting hardware architectures based on vector instructions. Our previous work is extended with a mechanism that provides complexity scalability to the system. Such a feature allows the coder to regulate the throughput achieved at the expense of slightly penalizing compression efficiency. Experimental results suggests that, when using the fastest speed, the method almost doubles the throughput of our previous engine while penalizing compression efficiency by about 10%

Combined coding of bit planes of images

The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.

Melanoma Identification with Content based Image Classification using Bit Plane Features

Augmented episodes of melanoma, a curable skin cancer variety of antagonistic nature, have stimulated the advancements in designing systems for computer aided diagnosis of the disease. Clinical diagnosis includes primary vetting of the symptoms followed by a biopsy and necessary medical examinations. However, computer based classification of the clinical images of dermoscopy have the potential to diminish the exertion of the dermatologist by offering a computer aided opinion independent of medical know-how. Assorted methods are proposed in recent times including the deep learning techniques for computer based melanoma recognition. But, most of the techniques have enhanced computational overhead which has added to the computational complexity of the entire system. In this work, the authors have attempted to design light-weight feature extraction techniques from high level bit planes of dermoscopic images by ignoring the noisy slices of bit planes for robust feature extraction. The proposed method of feature extraction is tested with three different classifiers for specificity and sensitivity outputs of the dermoscopic images. The results of classification have outclassed the performance of state-of-the-art feature extraction techniques.

Identifying traffic of same keys in cryptographic communications using fuzzy decision criteria and bit-plane measures

The use of same keys for different messages is not safe in cryptography to secure cryptographic communications even if encryption algorithm is strong enough and possesses good cryptographic properties. Such communications can be analyzed to find meaningful information by cryptanalysts or adversaries. Use of same keys may happens if keys are not managed in cipher systems appropriately by customers. One should evaluate ciphers thoroughly and assure for non-repetition of keys prior to its usage for secure communications. The paper presents a methodology to identify and segregate traffic of cryptographic communications of images encrypted with same keys by exploiting bit-plane image characteristics and applying Fuzzy decision criteria. Results presented in the paper shows that the proposed Fuzzy classification method is able to identify images encrypted with same keys successfully and it seems very useful to consider for various pattern recognition and image analysis problems.

Hierarchical Bit-Level Image Encryption Based on Chaotic Map and Feistel Network

In this paper, to avoid chosen-plaintext attacks and to enhance the security, SHA-256 hash value of the plain image is introduced to produce the initial values and parameters of the chaotic system, thus the produced keys and the ciphered image are highly sensitive to changes of the plain image. Based on the plain image related chaotic sequences, a hierarchical bit-level image encryption scheme is proposed by using an “Incomplete” Feistel network. In this scheme, the left four most significant bits (MSB) and the right four least significant bits (LSB) of each pixel are encrypted differently by the “Incomplete” Feistel network. During the encryption process, different bitplanes of gray image and color image are encrypted differently by the keystreams generated from the Logistic-Sine system (LSS) and the Lorenz system. The theoretical analysis and experiment results show that the proposed encryption algorithm is effective and secure enough to be used for image encryption.

Robust Visual Secret Sharing Scheme Applying to QR Code

Different color patterns of quick response (QR) codes, such as RGB, grayscale, and binary QR codes, are widely used in applications. In this paper, we propose a novel XOR-based visual secret sharing (VSS) scheme using grayscale QR codes as cover images and binary QR code as secret image. First, all the codewords of the secret QR code image are encoded into n temporary binary QR code images, which are substituted for the second significant bit planes of the grayscale QR code cover images to generate n shares. Each share is a grayscale QR code image, which can be decoded by a standard QR code decoder, so that it may not attract the attention of potential attackers when distributed in the public channel. The secret image can be recovered by XORing the codewords regions of QR codes which are extracted from the second significant bit planes of the grayscale shares. More importantly, the proposed scheme is robust to JPEG compression, addition of different noises, rotation, resizing, and cropping, which is useful in practice. The effectiveness and robustness of our scheme are shown by the experimental results. The application of QR code is suitable for wireless multimedia data security.

A selective bitplane image encryption scheme using chaotic maps

Partial encryption is one of the viable solutions for low power, high speed, real time secure multimedia communication. In this paper, a chaotic tent map based selective bitplane encryption technique is proposed for both gray scale and color images. After decomposing the original image into eight bitplanes, each bitplane is classified into either significant or non-significant category by defining a flexible threshold value of 0.3, deduced experimentally. Following this segregation, only the significant bitplanes are encrypted with the key stream sequences generated by a chaos-based pseudo-random binary number generator. The cipher image is then transmitted through public channel. The proposed scheme has three important contributions v.i.z. a) determination of significant bitplanes, b) encryption of only the significant bitplanes leading to reduction in computational complexity and c) elimination of the need for separate channel for transmitting the information about the significant bitplanes. It is shown that the proposed partial encryption scheme saves around 35% computation on the image database used here. Different types of attacks against this scheme are also analysed to show the robustness of this approach.

Thermal Image Encryption Based on Region of Interest (ROI) and Chaotic map

Most of the thermal image information concentrated on a particular region and 50% of image information stored in Most Significant Bit (MSB) plane. Based on these two strategy, proposed an thermal image encryption based on region of interest (ROI) and chaotic map. Firstly extract the MSB bit plane of thermal input image. This MSB bit plane undergo ROI calculation method. This method involves the partition of MSB bit plane into several 16*16 blocks. Apply the thresholding function to each block to separate the ROI blocks. Then selected part from input thermal image undergo XOR operation between random image generated from chaotic map. For lossless decryption at the receiver side, embedded the index of ROI blocks using LSB embedding algorithm. Simulation results shows the proposed algorithm achieves good security and more efficiency.

A Variational Model for Sea Image Enhancement

The purpose of sea image enhancement is to enhance the information of the waves, whose contrast is generally weak. Enhancement effect is often affected by impulse-type noise and non-uniform illumination. In this paper, we propose a variational model for sea image enhancement using a solar halo model and a Retinex model. This paper mainly makes the following three contributions: 1. Establishing a Retinex model with noise suppression ability in sea images; 2. Establishing a solar-scattering halo model through sea image bitplane analysis; 3. Proposing a variational enhancement model combining the Retinex and halo models. The experimental results show that our method has a significant enhancement effect on sea surface images in different illumination environments compared with typical methods.