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Interval algorithm for random number generation

IEEE TRANSACTIONS ON INFORMATION THEORY 1997年第2期43卷 599-611页

作者： Han, TS Hoshi, M Sch. of Inf. Syst. Univ. of Electro-Commun. Tokyo Japan Graduate School of Information Systems University of Electro-Communications Chofu Tokyo Japan

The problem of generating a random number with an arbitrary probability distribution by using a general biased M-coin is studied, An efficient and very simple algorithm based on the successive refinement of partitions of the unit interval [0, 1), which we call the interval algorithm, is proposed, A fairly tight evaluation on the efficiency is given, Generalizations of the interval algorithm to the following cases are investigated: 1) output sequence is independent and identically distributed (i.i.d.);2) output sequence is Markov;3) input sequence is Markov;4) input sequence and output sequence are both subject to arbitrary stochastic processes.

关键词： random number generation interval algorithm partitions entropy biased coin arithmetic coding

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Lossless generalized-LSB data embedding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2005年第2期14卷 253-266页

作者： Celik, MU Sharma, G Tekalp, AM Saber, E Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA Univ Rochester Dept Biostat & Computat Biol Rochester NY 14627 USA Koc Univ Coll Engn Istanbul Turkey Xerox Corp Webster NY 14580 USA

We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.

关键词： arithmetic coding conditional entropy coding context modeling data embedding data hiding least significant bit (LSB) modification watermark

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Context-Aware Linguistic Steganography Model Based on Neural Machine Translation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 868-878页

作者： Ding, Changhao Fu, Zhangjie Yang, Zhongliang Yu, Qi Li, Daqiu Huang, Yongfeng Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing 210000 Peoples R China Xidian Univ Minist Educ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Linguistic steganography based on text generation is a hot topic in the field of text information hiding. Previous studies have managed to improve the syntactic quality of steganography texts using natural language processing techniques based on deep learning, but their steganography models still lack the ability to control the semantic and contextual characteristics in texts, which is caused by the shortage of relevant information they can obtain. This results in a great decline in the imperceptibility of steganographic texts. To address the problem, we propose a context-aware linguistic steganography method based on neural machine translation called NMT-Stega. The model generates translation containing secret messages based on the neural machine translation model with semantic fusion and language model reference units. In this way, the semantics and contexts of translation are controlled by the additional semantic and contextual features acquired from the text to be translated. Also, a new encoding that combines arithmetic coding with a waiting mechanism is proposed in our model. This method solves the low embedding capacity problem of waiting mechanism while ensuring the semantic and contextual characteristics of steganographic text are less modified. Experimental results show that our model outperforms the previous models and encoding methods in semantic correlation, embedding capacity and imperceptibility.

关键词： Neural networks linguistic steganography neural machine translation arithmetic coding

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An algorithm for universal lossless compression with side information

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IEEE TRANSACTIONS ON INFORMATION THEORY 2006年第9期52卷 4008-4016页

作者： Cai, Haixiao Kulkarni, Sanjeev R. Verdu, Sergio Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

This paper proposes a new algorithm based on the Context-Tree Weighting (CTW) method for universal compression of a finite-alphabet sequence x(1)(n) with side information y(1)(n) available to both the encoder and decoder. We prove that with probability one the compression ratio converges to the conditional entropy rate for jointly stationary ergodic sources. Experimental results with Markov chains and English texts show the effectiveness of the algorithm.

关键词： arithmetic coding conditional entropy context tree weighting method hidden Markov process source coding universal lossless data compression

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Lossless nibbled data embedding scheme based on difference expansion

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IMAGE AND VISION COMPUTING 2008年第5期26卷 632-638页

作者： Lu, Tzu-Chuen Chang, Chin-Chen Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan Feng Chia Univ Dept Comp Sci & Informat Engn Taichung 40724 Taiwan

This paper proposes a lossless data embedding scheme of great payload capacity and good image quality, which is based on difference expansion. In this scheme, every pixel in a host image is divided into two nibbles and each nibble pair between two adjacent pixels can be used to hide a secret message. In order to completely recover the host image, the arithmetic coding is adopted based on prediction by partial matching (PPM) model to compress the restored information. This proposed scheme has been successfully applied to different images. According to the experimental results, embedded information can be extracted correctly and quickly from the embedded image. In addition, the proposed scheme can not only hide a large amount of information in a host image without making noticeable distortion, but can also completely restore the host image from the embedded image. (C) 2007 Elsevier B.V. All rights reserved.

关键词： lossless data embedding difference expansion nibble arithmetic coding prediction by partial matching

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Securing Multimedia Content Using Joint Compression and Encryption

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IEEE MULTIMEDIA 2013年第4期20卷 50-61页

作者： Pande, Amit Mohapatra, Prasant Zambreno, Joseph Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Iowa State Univ Dept Elect & Comp Engn Ames IA USA

Algorithmic parameterization and hardware architectures can ensure secure transmission of multimedia data in resource-constrained environments such as wireless video surveillance networks, telemedicine frameworks for distant health care support in rural areas, and Internet video streaming. Joint multimedia compression and encryption techniques can significantly reduce the computational requirements of video processing systems. The authors present an approach to reduce the computational cost of multimedia encryption while also preserving the properties of compressed video. A hardware-amenable design of the proposed algorithms makes them suitable for real-time embedded multimedia systems. This approach alleviates the need for additional hardware for encryption in resource-constrained scenarios and can be otherwise used to augment existing encryption methods used for content delivery on the Internet or in other applications. This work shows how two compression blocks for video coding--a modified frequency transform (called a secure wavelet transform or SWT) and a modified entropy coding scheme (called a chaotic arithmetic coding or CAC)--can be used for video encryption. Experimental results are shown for selective encryption using the proposed schemes.

关键词： Encryption Content Analysis Discrete Wavelet Transforms Streaming Media Multimedia Communication Encoding arithmetic coding Multimedia Video Encryption Joint coding And Encryption Embedded Systems Discrete Wavelet Transform

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Lossless Compression on MRI Images Using SWT

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JOURNAL OF DIGITAL IMAGING 2014年第5期27卷 594-600页

作者： Anusuya, V. Raghavan, V. Srinivasa Kavitha, G. PSR Engn Coll Dept Comp Sci & Engn Sivakasi India Loyola Inst Technol Chennai Tamil Nadu India AAA Coll Engn & Technol Sivakasi Tamil Nadu India

Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. The system proposes to implement a lossless codec using an entropy coder. 3D medical images are decomposed into 2D slices and subjected to 2D-stationary wavelet transform (SWT). The decimated coefficients are compressed in parallel using embedded block coding with optimized truncation of the embedded bit stream. These bit streams are decoded and reconstructed using inverse SWT. Finally, the compression ratio (CR) is evaluated to prove the efficiency of the proposal. As an enhancement, the proposed system concentrates on minimizing the computation time by introducing parallel computing on the arithmetic coding stage as it deals with multiple subslices.

关键词： Stationary wavelet transform EBCOT arithmetic coding Bit plane coding

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Two-Symbol FPGA Architecture for Fast arithmetic Encoding in JPEG 2000

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2012年第2期69卷 213-224页

作者： Kumar, Nandini Ramesh Xiang, Wei Wang, Yafeng Univ So Queensland Fac Engn & Surveying Toowoomba Qld 4350 Australia

JPEG 2000 is one of the most popular image compression standards offering significant performance advantages over previous image standards. High computational complexity of the JPEG 2000 algorithms makes it necessary to employ methods that overcomes the bottlenecks of the system and hence an efficient solution is imperative. One such crucial algorithms in JPEG 2000 is arithmetic coding and is completely based on bit level operations. In this paper, an efficient hardware implementation of arithmetic coding is proposed which uses efficient pipelining and parallel processing for intermediate blocks. The idea is to provide a two-symbol coding engine, which is efficient in terms of performance, memory and hardware. This architecture is implemented in Verilog hardware definition language and synthesized using Altera field programmable gate array. The only memory unit used in this design is a FIFO (first in first out) of 256 bits to store the CX-D pairs at the input, which is negligible compared to the existing arithmetic coding hardware designs. The simulation and synthesis results show that the operating frequency of the proposed architecture is greater than 100 MHz and it achieves a throughput of 212 Msymbols/sec, which is double the throughput of conventional one-symbol implementation and enables at least 50% throughput increase compared to the existing two-symbol architectures.

关键词： JPEG 2000 arithmetic coding MQ-Coder FPGA Two-symbol architecture

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FIVQ algorithm for interference hyper-spectral image compression

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OPTICS COMMUNICATIONS 2014年 322卷 97-104页

作者： Wen, Jia Ma, Caiwen Zhao, Junsuo Chinese Acad Sci Sci Integrated Informat Syst Lab Inst Software Beijing 100190 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASlS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences. (C) 2014 Elsevier B.V. All rights reserved

关键词： Image compression Vector quantization LBG algorithm arithmetic coding

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Cover Reproducible Steganography via Deep Generative Models

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第5期20卷 3787-3798页

作者： Chen, Kejiang Zhou, Hang Wang, Yaofei Li, Menghan Zhang, Weiming Yu, Nenghai Univ Sci & Technol China CAS Key Lab Electrospace Informat Sch Cyber Sci & Technol Anhui Prov Key Lab Cyberspace Secur Situat Aware Hefei 230026 Anhui Peoples R China Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Hefei Univ Technol Sch Comp Sci Hefei 230002 Anhui Peoples R China

Whereas cryptography easily arouses attacks by means of encrypting a secret message into a suspicious form, steganography is advantageous for its resilience to attacks by concealing the message in an innocent-looking cover signal. Minimal distortion steganography, one of the mainstream steganography frameworks, embeds messages while minimizing the distortion caused by the modification on the cover elements. Due to the unavailability of the original cover signal for the receiver, message embedding is realized by finding the coset leader of the syndrome function of steganographic codes migrated from channel coding, which is complex and has limited performance. Fortunately, deep generative models and the robust semantic of generated data make it possible for the receiver to perfectly reproduce the cover signal from the stego signal. With this advantage, we propose cover-reproducible steganography where the source coding, e.g., arithmetic coding, serves as the steganographic code. Specifically, the decoding process of arithmetic coding is used for message embedding and its encoding process is regarded as message extraction. Taking text-to-speech and text-to-image synthesis tasks as two examples, we illustrate the feasibility of cover-reproducible steganography. Steganalysis experiments and theoretical analysis are conducted to demonstrate that the proposed methods outperform the existing methods in most cases.

关键词： Steganography Distortion Encoding Receivers Codes Costs Task analysis Steganography reproducible arithmetic coding text-to-speech text-to-image generative model

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