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Lightweight Steganography Detection Method Based on Multiple Residual Structures and transformer

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Chinese Journal of Electronics 2024年第4期33卷 965-978页

作者： Hao LI Yi ZHANG Jinwei WANG Weiming ZHANG Xiangyang LUO Key Laboratory of Cyberspace Situation Awareness of Henan Province Nanjing University of Information Science & Technology University of Science and Technology of China

Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features, yielding higher detection efficiency compared to manually designed steganography detection methods. Detection methods based on convolutional neural network frameworks can extract global features by increasing the network's depth and width. These frameworks are not highly sensitive to global features and can lead to significant resource consumption. This manuscript proposes a lightweight steganography detection method based on multiple residual structures and transformer(Res Former). A multi-residuals block based on channel rearrangement is designed in the preprocessing layer. Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability. A lightweight convolutional and transformer feature extraction backbone is constructed, which reduces the computational and parameter complexity of the network by employing depth-wise separable convolutions. This backbone integrates local and global image features through the fusion of convolutional layers and transformer, enhancing the network's ability to learn global features and effectively enriching feature diversity. An effective weighted loss function is introduced for learning both local and global features, Bias Loss loss function is used to give full play to the role of feature diversity in classification, and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features. Based on Boss Base-1.01, BOWS2 and ALASKA#2, extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms, employing both classical and state-of-theart steganalysis techniques. The experimental results demonstrate that compared to the SRM, SRNet, Sia Steg Net,CSANet, LWENet, and Sia IRNet methods, the proposed Res

关键词： Q-factor Steganography Accuracy Convolution Computational modeling transform coding transformers

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Image coding Image color analysis Quaternions Forensics transform coding Color Feature extraction

A Fast Algorithm of the DCT and IDCT for VLSI Implementation

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A Fast Algorithm of the DCT and IDCT for VLSI Implementation

Lattice Vector Quantization Applied to Speech and Audio coding

1996 3rd International Conference on Signal Processing(ICSP’96)

作者： Hong Ying Hou Zhaohuan Institute of Acoustics Chinese Academy of Sciences

ISBN: (纸本)0780329120

＜正＞Since DCT performs very close to the statistically optimal Karhunen-Loeve transform（KLT）,it is widely used in digital signal processing,especially for speech and image data *** DCT algorithms and VLSI architectures for real-time computation capabilities are required *** is known that VLSI implementation of distributed arithmetic is very efficient for computing ***,an algorithm is presented to convert the DCT and inverse DCT（IDCT） to *** implementation of the algorithm has same advantage as any implementation using distributed arithmetic.

关键词： VLSI discrete cosine transforms inverse problems transform coding digital signal processing chips data compression speech coding image coding digital arithmetic convolution number theory IDCT DCT VLSI implementation fast algorithm digital signal processing statistically optimal Karhunen-Loeve transform image data compression speech compression VLSI architectures real-time computation distributed arithmetic inverse DCT skew-convolution number theoretic transforms

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ZTE Communications 2012年第2期10卷 25-33页

作者： Minjie Xie ZTE USA Inc. Richardson TX 75080 USA

Lattice vector quantization （LVQ） has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages： It has a simple and fast encoding process, and it significantly reduces the amount of memory required. Therefore, LVQ is suitable for use in low-complexity speech and audio coding. In this paper, we describe the basic concepts of LVQ and its advantages over conventional vector quantization. We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations （SDOs）.

关键词： Vector quantization lattice vector quantization speech and audio coding transform coding

维普期刊数据库

Hot topic:Review of the current and future technologies for video compression

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第1期11卷 1-13页

作者： Lu YU Jian-peng WANG Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Key Laboratory of lntegrated lnformation Network Technology of Zhejiang Province Hangzhou 310027 China

Many important developments in video compression technologies have occurred during the past two decades. The block-based discrete cosine transform with motion compensation hybrid coding scheme has been widely employed by most available video coding standards, notably the ITU-T H.26x and ISO/IEC MPEG-x families and video part of China audio video coding standard (AVS). The objective of this paper is to provide a review of the developments of the four basic building blocks of hybrid coding scheme, namely predictive coding, transform coding, quantization and entropy coding, and give theoretical analyses and summaries of the technological advancements. We further analyze the development trends and perspectives of video com- pression, highlighting problems and research directions.

关键词： Video compression Predictive coding transform coding Quantization, Entropy coding Theoretical analysis

维普期刊数据库

VLSI Architecture of EBCOT Tier-2 Encoder for JPEG2000

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VLSI Architecture of EBCOT Tier-2 Encoder for JPEG2000