Coding Algorithm Research Articles

Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses.

Morphing Quantum Codes

We introduce a morphing procedure that can be used to generate new quantum codes from existing quantum codes. In particular, we morph the 15-qubit Reed-Muller code to obtain a $[\![10,1,2]\!]$ code that is the smallest known stabilizer code with a fault-tolerant logical $T$ gate. In addition, we construct a family of hybrid color-toric codes by morphing the color code. Our code family inherits the fault-tolerant gates of the original color code, implemented via constant-depth local unitaries. As a special case of this construction, we obtain toric codes with fault-tolerant multi-qubit control-$Z$ gates. We also provide an efficient decoding algorithm for hybrid color-toric codes in two dimensions, and numerically benchmark its performance for phase-flip noise. We expect that morphing may also be a useful technique for modifying other code families such as triorthogonal codes.

High capacity video hiding based on multi-resolution stationary wavelet transform and hybrid-matrix decomposition techniqueset Transform and hybrid-matrix decomposition techniques

Internet simplified digital data transferring. This data needs to be secured; so securing digital data becomes an important concern. Steganography provides security for data by inserting it into a cover and concealing it. In this paper, a steganography algorithm was introduced. This algorithm used the stationary wavelet transform (SWT) and اhybrid-matrix decomposition techniques, singular value decomposition (SVD) and QR factorization to conceal a video in another video. The algorithm performance was measured with reference to the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) for the cover and the secret videos. The algorithm successfully hid a video in another cover video and both are of the same size; the hiding capacity was 100%. The algorithm achieved a SSIM that reached 0.97 and a high PSNR value that reached 68.8 proving that the imperceptibility of the proposed algorithm is very high. The comparative analysis shows that the suggested algorithm achieved higher imperceptibility than the other state-of-the-art algorithms regarding the average PSNR. The enhanced version of the proposed method was more robust against different types of attacks.

Adaptive Belief Propagation Decoding of CRC Concatenated NR LDPC and Polar Codes

In this paper, we propose a modified adaptive belief propagation (ABP) algorithm, which is referred to as the random ABP (R-ABP) algorithm, for decoding short channel codes adopted in the fifth generation (5G) New Radio (NR) wireless systems. Based on the cyclic redundancy check (CRC) concatenation structure of 5G channel codes, we can take partial or even all CRC bits into iterative R-ABP decoding to improve the error correction performance, while the error detection capability can still be guaranteed by the remaining CRC bits and a proposed threshold-based acceptance criterion. We call this improved R-ABP decoding the threshold-and-CRC-aided R-ABP (TCA-R-ABP) decoding. The simulation results show that our proposed TCA-R-ABP algorithm outperforms the state-of-the-art decoding algorithms for many 5G low-density parity-check (LDPC) and polar codes. Moreover, the proposed TCA-R-ABP algorithm leads to a unified decoder for LDPC and polar codes, which has the potential to be a lower-complexity decoder over the combination of the belief propagation (BP) and CRC-aided successive cancellation list (CA-SCL) decoders.

Improved encoding structure and decoding algorithms for spinal codes

To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding (A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination (AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

Generic Decoding in the Sum-Rank Metric

We propose the first non-trivial generic decoding algorithm for codes in the sum-rank metric. The new method combines ideas of well-known generic decoders in the Hamming and rank metric. For the same code parameters and number of errors, the new generic decoder has a larger expected complexity than the known generic decoders for the Hamming metric and smaller than the known rank-metric decoders. Furthermore, we give a formal hardness reduction, providing evidence that generic sum-rank decoding is computationally hard. As a by-product of the above, we solve some fundamental coding problems in the sum-rank metric: we give an algorithm to compute the exact size of a sphere of a given sum-rank radius, and also give an upper bound as a closed formula; and we study erasure decoding with respect to two different notions of support.

Developing Algorithms for Image Steganography and Increasing the Capacity Depending on Choosing the Best Pixels

Steganography is a vital technique for transferring confidential information via an insecure network. In addition, digital images are used as a cover to communicate sensitive information. The Least Significant Bit (LSB) method is one of the simplest ways to insert secret data into a cover image. In this paper, the secret text is compressed twice by an Arithmetic coding algorithm, and the resulting secret bits are hidden in the cover pixels of the image corresponding to the pixels of each of the following three methods, one of three methods is used in each experiment: The first method, the edges of the image are modified to increase the number of edges, in the second method the lighter-colored regions are selected, and in the third method, the two methods are combined together to increase security and keep the secret message unrecognized. Hiding in each of the previous methods is done by using the LSB technique in the last 2-bit. The correction approach is used to increase the stego image's imperceptibility. The experimental results show that with an average message size of 29.8 kb, the average Peak Signal-to-Noise Ratio (PSNR) for the second proposed (Light regions) method equals 62.76 dB and for the third proposed (Edge and region) method equals 62.72 dB, which is a reasonable result when compared to other steganographic techniques.

Implementasi Algoritma Elias Gamma Code Untuk Kompresi File Audio Hasil Rekaman Dari Aplikasi Wesing Karaoke

In an era of increasingly sophisticated technology, such as today, the high activity of data storage and transmission creates a problem which can lead to wasteful storage space and slow data transmission processes online or offline. The longer data storage will result in accumulating more and more data. This data accumulation becomes a problem, especially from the availability of storage space. Along with current technological developments, the ability to process data is increasingly developing, namely by utilizing compression methods which are expected to answer these problems. The Elias Gamma Code algorithm is used to compress audio files recorded from the Wesing Karaoke application by reducing the file size from the beginning. So that by applying the Elias Gamma Code algorithm to compress audio files, it can reduce the optimal storage capacity on smartphones (cellphones).

Kompresi File Teks dengan Menggunakan Kombinasi Squitur dan Elias Gamma Code

Text files have a large size that affects the storage space and the sending process. This size is a factor in how much storage space will be used and how long it will take to send files. The longer the text, the larger the size. Large sizes can be overcome by doing the compression process. Compression is done to reduce the size of a file. Compression algorithms that can be used are sequitur algorithm and elias gamma code algorithm. The advantage of the sequitur algorithm is to replace 2 characters side by side with 1 terminal character in the character set, while the advantage of the elias gamma code algorithm is to replace the initial bit of the character with a gamma elias code bit. The two algorithms will be combined to produce a smaller size than the previous one

Application of Huffman Algorithm and Unary Codes for Text File Compression

Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huffman algorithms and unary code can solve this problem. By compressing the selected data also encrypts it as an extra security. The Huffman algorithm is a lossless compression algorithm or a technique that does not change the original data, by converting the unit of data content into bits. So this algorithm is widely used in the compression process. The Unary Codes algorithm is also a lossless compression technique that is generally used by combining several modification techniques. In this unary codes algorithm, each symbol in the string will be searched for its frequency. Then sorted from the last order (descending). The use of these two text data compression techniques results in a file size that is smaller than the original but can be returned to the original data

Characterising Eye Movement Events with an Unsupervised Hidden Markov Model.

Eye-tracking allows researchers to infer cognitive processes from eye movements that are classified into distinct events. Parsing the events is typically done by algorithms. Here we aim at developing an unsupervised, generative model that can be fitted to eye-movement data using maximum likelihood estimation. This approach allows hypothesis testing about fitted models, next to being a method for classification. We developed gazeHMM, an algorithm that uses a hidden Markov model as a generative model, has few critical parameters to be set by users, and does not require human coded data as input. The algorithm classifies gaze data into fixations, saccades, and optionally postsaccadic oscillations and smooth pursuits. We evaluated gazeHMM's performance in a simulation study, showing that it successfully recovered hidden Markov model parameters and hidden states. Parameters were less well recovered when we included a smooth pursuit state and/or added even small noise to simulated data. We applied generative models with different numbers of events to benchmark data. Comparing them indicated that hidden Markov models with more events than expected had most likely generated the data. We also applied the full algorithm to benchmark data and assessed its similarity to human coding and other algorithms. For static stimuli, gazeHMM showed high similarity and outperformed other algorithms in this regard. For dynamic stimuli, gazeHMM tended to rapidly switch between fixations and smooth pursuits but still displayed higher similarity than most other algorithms. Concluding that gazeHMM can be used in practice, we recommend parsing smooth pursuits only for exploratory purposes. Future hidden Markov model algorithms could use covariates to better capture eye movement processes and explicitly model event durations to classify smooth pursuits more accurately.

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units’ (sub-CUs’) information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).

B-Spot: Blockchain and Steganography based Robust and Secure Photo Transmission Mechanism

In this paper, a Steganography and Blockchain based robust and secure photo transmission mechanism, named B-Spot, is proposed. At the sender side, firstly, a 3-3-2 LSB image steganography algorithm is used to hide a secret photo into a cover photo. Therefore, the existence of the secret photo is concealed. Then the stego-image is broken down into pixels and embedded into blocks. Blocks are connected by the hash values forming a blockchain. Any modifications on the blocks are reflected by the breakage of the chain, which makes the mechanism tamper-evident. Another copy of the blockchain is stored in a hash table for the latter recovery process. The blockchain and the hash table can be transmitted via any network. When the receiver receives the data, it firstly executes the verification process to check integrity of the blockchain. Then the lost and tampered blocks are recovered by referring to the hash table, which makes the mechanism more robust to noises. Finally, the stego-image is reconstructed from the recovered blockchain. Then the receiver can obtain the secret photo following the extraction algorithm. The simulation results show that the proposed mechanism has high data capacity, better imperceptibility, reasonable computing time, and strong robustness to noise. This mechanism adds an extra layer of security and robustness to the existing schemes.

A new chase-type soft-decision decoding algorithm for Reed-Solomon codes

A new Chase-type soft-decision decoding algorithm for Reed-Solomon codes is proposed, referred to as tree-based Chase-type algorithm. The proposed tree-based Chase-type algorithm takes the set of all vectors as the set of testing patterns, and hence definitely delivers the most-likely codeword provided that the computational resources are allowed. All the testing patterns are arranged in an ordered rooted tree according to the likelihood bounds of the possibly generated codewords, which is an extension of Wu and Pados’ method from binary into q-ary linear block codes. While performing the algorithm, the ordered rooted tree is constructed progressively by adding at most two leafs at each trial. The ordered tree naturally induces a sufficient condition for the most-likely codeword. That is, whenever the tree-based Chase-type algorithm exits before a preset maximum number of trials is reached, the output codeword must be the most-likely one. But, in fact, the algorithm can be terminated by setting a discrepancy threshold instead of a maximum number of trials. When the tree-based Chase-type algorithm is combined with Guruswami-Sudan (GS) algorithm, each trial can be implement in an extremely simple way by removing from the gradually updated Gröbner basis one old point and interpolating one new point. Simulation results show that the tree-based Chase-type algorithm performs better than the recently proposed Chase-type algorithm by Bellorado et al. with less trials (on average) given that the maximum number of trials is the same.

Modeling of fractional differential equation in cloud computing image fusion algorithm

Abstract In order to solve the problems of poor image quality, low definition and loss of image information in traditional algorithms, a modeling research of fractional differential equation in cloud computing image fusion algorithm is proposed. Firstly, the method of image denoising and enhancement under the framework of fractional calculus theory and the improved algorithm based on it are discussed. A series of difficult problems in the image processing method based on fractional calculus are discussed. Then, the large data video image in cloud computing environment is fused in scale space through fractional differential equation, and the fused image is decomposed by lifting fractional differential equation to obtain the low-frequency subband coefficients and high-frequency subband coefficients in different scale space. For the low-frequency subband coefficients and high-frequency subband coefficients, their respective fusion schemes are given to obtain the lifting static small transform coefficients of the fused image. The fusion effect of the proposed algorithm is tested from both subjective and objective aspects.The results show that the entropy value of this algorithm is 7.1450, slightly higher than sparse coding algorithm and random walk algorithm, which shows that the fusion processing by this algorithm will not lose the amount of information contained in the image. Therefore, the image denoising and enhancement algorithm of fractional differential equation proposed in this paper has good subjective fusion effect, good clarity and quality of the fused image, and will not lose the information contained in the image.

A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography

High-speed wireless network technologies play a vital role in autonomous vehicle communication systems (AVS), i.e., IoV (Internet of Vehicles), and enhance the effectiveness and reliability of the communication network. Since the size of IoV communication is rapidly growing, it is now an essential part of our regular routines. Due to its dynamic nature and massive size, it is essential to avoid any miss-communication and protect the data privacy of each vehicle.IoVallowsAVS to link remotely to each other and to various platforms and groups that may be using the same communications system. This quality link between vehicles and the need for information exchanges among vehicles introduces vulnerability and threats that can be exploited by cybercriminals. This research emphasizes on enhancing data security in real-time IoV communication. It presents a trust-driven privacy method using encryption and steganography for IoV. This method uses an Efficient Algorithm for Secure Transmission (EAST) that integrates encryption and steganography algorithms. The proposed EAST is then compared to the various current state of arts, i.e., AES (Advanced Encryption Standard), DES (Data Encryption Standard), G-DES (Generalized DES), and Standard LSB on the basis of various performance measuring parameters, i.e., encryption and decryption time, efficiency, avalanche effect, PSNR, and file cover size. Experimental results are promising for the proposed EAST method, which reported a better time efficiency of 0.86 ms, avalanche effect of 58.81%, and PSNR of 78.58%, compared to the current state arts.

TOWARD HOLISTIC DATA SCIENCE EDUCATION

Holistic data science education places data science in the context of real world applications, emphasizing the purpose for which data were collected, the pedigree of the data, the meaning inherent in the daa, the deploying of sustainable solutions, and the communication of key findings for addressing the original problem. As such it spends less emphasis on coding, computing, and high-end black-box algorithms. We argue that data science education must move toward a holistic curriculum, and we provide examples and reasons for this emphasis.

Adaptive coding for DNA storage with high storage density and low coverage

The rapid development of information technology has generated substantial data, which urgently requires new storage media and storage methods. DNA, as a storage medium with high density, high durability, and ultra-long storage time characteristics, is promising as a potential solution. However, DNA storage is still in its infancy and suffers from low space utilization of DNA strands, high read coverage, and poor coding coupling. Therefore, in this work, an adaptive coding DNA storage system is proposed to use different coding schemes for different coding region locations, and the method of adaptively generating coding constraint thresholds is used to optimize at the system level to ensure the efficient operation of each link. Images, videos, and PDF files of size 698 KB were stored in DNA using adaptive coding algorithms. The data were sequenced and losslessly decoded into raw data. Compared with previous work, the DNA storage system implemented by adaptive coding proposed in this paper has high storage density and low read coverage, which promotes the development of carbon-based storage systems.

QPlayer: Lightweight, scalable, and fast quantum simulator

AbstractWith the rapid evolution of quantum computing, digital quantum simulations are essential for quantum algorithm verification, quantum error analysis, and new quantum applications. However, the exponential increase in memory overhead and operation time is challenging issues that have not been solved for years. We propose a novel approach that provides more qubits and faster quantum operations with smaller memory than before. Our method selectively tracks realized quantum states using a reduced quantum state representation scheme instead of loading the entire quantum states into memory. This method dramatically reduces memory space ensuring fast quantum computations without compromising the global quantum states. Furthermore, our empirical evaluation reveals that our proposed idea outperforms traditional methods for various algorithms. We verified that the Grover algorithm supports up to 55 qubits and the surface code algorithm supports up to 85 qubits in 512 GB memory on a single computational node, which is against the previous studies that support only between 35 qubits and 49 qubits.