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ByteNet: Rethinking Multimedia File Fragment Classification Through Visual Perspectives

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1305-1319页

作者： Liu, Wenyang Wu, Kejun Liu, Tianyi Wang, Yi Yap, Kim-Hui Chau, Lap-Pui Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China

Multimedia file fragment classification (MFFC) aims to identify file fragment types, e.g., image/video, audio, and text without system metadata. It is of vital importance in multimedia storage and communication. Existing MFFC methods typically treat fragments as 1D byte sequences and emphasize the relations between separate bytes (interbytes) for classification. However, the more informative relations inside bytes (intrabytes) are overlooked and seldom investigated. By looking inside bytes, the bit-level details of file fragments can be accessed, enabling a more accurate classification. Motivated by this, we first propose Byte2Image, a novel visual representation model that incorporates previously overlooked intrabyte information into file fragments and reinterprets these fragments as 2D grayscale images. This model involves a sliding byte window to reveal the intrabyte information and a rowwise stacking of intrabyte n-grams for embedding fragments into a 2D space. Thus, complex interbyte and intrabyte correlations can be mined simultaneously using powerful vision networks. Additionally, we propose an end-to-end dual-branch network ByteNet to enhance robust correlation mining and feature representation. ByteNet makes full use of the raw 1D byte sequence and the converted 2D image through a shallow byte branch feature extraction (BBFE) and a deep image branch feature extraction (IBFE) network. In particular, the BBFE, composed of a single fully-connected layer, adaptively recognizes the co-occurrence of several some specific bytes within the raw byte sequence, while the IBFE, built on a vision transformer, effectively mines the complex interbyte and intrabyte correlations from the converted image. Experiments on the two representative benchmarks, including 14 cases, validate that our proposed method outperforms state-of-the-art approaches on different cases by up to 12.2%.

关键词： Feature extraction Visualization Correlation Multimedia communication Media Gray-scale Discrete cosine transforms Symbols Accuracy transform coding Multimedia file fragment classification multimedia analysis deep learning computer vision

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Scalable Point Cloud Attribute Compression

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 889-899页

作者： Zhang, Junteng Wang, Jianqiang Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China

This article develops a Scalable Point Cloud Attribute Compression solution, termed ScalablePCAC. In a two-layer example, ScalablePCAC uses the standard G-PCC at the base layer to directly encode the thumbnail point cloud that is downscaled from the original input, and a learning-based model at the enhancement layer to compress and restore the full-resolution input point cloud conditioned on the base layer reconstruction. As such, the base layer provides a coarse reconstruction of the input point cloud and the enhancement layer further improves the quality. We then adopt a cross-layer rate allocation strategy that flexibly determines the resolution downscaling factor, the quantization parameter of the base layer, and the quality controlling factor of the enhancement layer to adapt the bitrate of the two layers for approximately optimal Rate-Distortion (R-D) performance. We conduct extensive experiments on popular point clouds following the MPEG common test conditions. Results demonstrate that the proposed ScalablePCAC achieves >10% BD-BR reduction against the latest G-PCC version 22 (TMC13v22) on the Y component;it also significantly outperforms existing learning-based solutions for point cloud attribute compression, e.g., compared with a recent work showing state-of-the-art performance, it achieves >20% BD- BR reduction.

关键词： Point cloud compression Image coding transform coding Three-dimensional displays Image reconstruction Scalability Rate-distortion 3D Point cloud attribute compression scalability MPEG G-PCC

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Reversible Data Hiding in Encrypted JPEG Images With Polynomial Secret Sharing for IoT Security

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 1098-1108页

作者： Xu, Shuying Horng, Ji-Hwei Chang, Ching-Chun Chang, Chin-Chen Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 40724 Taiwan Natl Quemoy Univ Dept Elect Engn Kinmen 892009 Taiwan Feng Chia Univ Informat & Commun Secur Res Ctr Taichung 40724 Taiwan

Crypto-space reversible data hiding (RDH) has emerged as an effective technique for transmitting secret information over the Internet. However, most existing schemes are designed for uncompressed images, while almost all images are processed and transmitted in compressed formats. There is an urgent need to develop methods for compressed images, such as joint photographic experts group (JPEG). In this article, we propose an RDH in encrypted JPEG images, where the bitstreams of alternating current (AC) coefficients and the secret data are mapped to numbers over Galois field. The obtained numbers are then utilized to conduct a polynomial for secret sharing. By reproduction into secret shares, the AC coefficients and the secret data are secured. In addition, a block sorting strategy is used to reduce image distortion under low data payload. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods in embedding capacity while preserving the file size and conforming to the JPEG format.

关键词： Cryptography transform coding Polynomials Image coding Encryption Internet of Things Galois fields Media Receivers Payloads Crypto-space steganography Galois field joint photographic experts group (JPEG) reversible data hiding (RDH) secret sharing

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Robust Image Steganography via Color Conversion

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1399-1408页

作者： Li, Qi Ma, Bin Fu, Xianping Wang, Xiaoyu Wang, Chunpeng Li, Xiaolong Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci CtrMinist EducShandong Prov Ke Natl Supercomp Ctr JinanKey Lab Computing Power N Jinan 250000 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Fundamental Res Ctr Comp Sci Jinan 250000 Peoples R China Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

In this paper, we propose a robust image steganography method utilizing color conversion, leveraging de-colorization and colorization models to achieve covert transmission of secret information. The motivation is to use color conversion of the stego image to conceal steganographic behavior. For the sender, secret information is embedded into the color cover image using a robust embedding algorithm based on quaternion exponent moments. The stego images are then de-colorized to obtain grayscale images, which can be transmitted over public channels. For the receiver, a corresponding colorization network is designed to reconstruct the stego image and extract the secret information. Additionally, an attack module using Gaussian noise is implemented to enhance the robustness of the proposed steganography. Given a color image, its grayscale version can be chosen from various options, making it difficult for attackers to detect steganographic activity as long as the generated grayscale image appears normal and meaningful. Extensive simulation results demonstrate the feasibility and scalability of the proposed steganography method.

关键词： Steganography Image color analysis Gray-scale Watermarking Security Data mining transform coding Image steganography de-colorization and colorization color conversion robust embedding algorithm

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Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 11-24页

作者： Li, Ming-Che Ghosh, Archisman Sen, Shreyas Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Recent expansions in multimedia devices for many applications, such as surveillance, self-driving cars, and healthcare, gather enormous amounts of real-time images for processing and inference. The images are first compressed using compression schemes, like joint photographics experts group (JPEG), before processing to reduce storage costs and additional power requirements for transmitting the captured data in this era of emerging ultra-wideband communication and human-body communication. The JPEG algorithm realizes image compression using simplistic matrix manipulations, making it preferable for hardware implementations. Furthermore, due to inherent error resilience and imperceptibility in images, JPEG can be approximated to reduce the required computation/processing power and area. This work demonstrates the first end-to-end approximation computing-based optimization of JPEG hardware using 1) an approximate division realized using bit-shift operators to reduce the complexity of the computationally intensive quantization block;2) loop perforation;and 3) precision scaling on top of a multiplier-less fast discrete cosine transform (DCT) architecture to achieve an extremely energy-efficient JPEG compression unit which will be a perfect fit for power/bandwidth-limited scenario. Furthermore, a gradient descent-based heuristic composed of two conventional approximation strategies, i.e., precision scaling and loop perforation, is implemented for tuning the degree of approximation to tradeoff energy consumption with the quality degradation of the decoded image. The entire register-transfer level (RTL) design is coded in Verilog HDL, synthesized using the industry-standard tool, mapped to TSMC 65nm CMOS technology, and simulated using Cadence Spectre Simulator under 25 degrees C, typical/typical (TT) corner. The approximate division approach in the quantization block achieved around 28% reduction in the active design area. The heuristic-based approximation technique combine

关键词： Image coding transform coding Discrete cosine transforms Quantization (signal) Hardware Computer architecture Energy efficiency Approximate computing approximate divider energy efficient image sensor in-sensor analytics loop perforation precision scaling

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Steganalysis Network With Two-Branch Preprocessing for Spatial and JPEG Domains

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1451-1463页

作者： He, Jian Weng, Shaowei Yu, Lifang Chen, Dewang Fujian Univ Technol Sch Elect Elect Engn & Phys Fuzhou 350118 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Beijing Inst Graphic Commun Dept Informat Engn Beijing 100026 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China

Considering that the nature of the stego signal caused by spatial domain steganography and joint photographic experts group (JPEG) domain steganography is different, existing deep-learning steganalysis networks typically cannot work well in both spatial and JPEG domains. We propose a unified steganalysis network named ESNet to effectively preserve and identify the stego signal from spatial and JPEG domains. Specifically, dual-branch preprocessing extracts noise residuals by using fixed SRM kernels (branch 1) and randomly initialized kernels (branch 2), fuses the features from two branches and exchanges the fused complementary information through two carefully designed bidirectional fusion blocks, thereby effectively enhancing the signal-to-noise ratio. During feature extraction, considering that low-level features, such as texture and edge, are indispensable for steganalysis, we gather multi-level feature maps at different layers of the network to provide richer feature representations and merge them by using a multi-level feature fusion module, which learns the weight of different features in single-level feature map to enhance the expression of steganographic features. During classification, the multi-scale attention pooling module is employed to extract multi-scale features by designing convolution kernels of different sizes. After concatenating features of different scales, gated channel transformation is exploited to weight the importance of each channel to further strengthen the representations of steganographic features. Finally, stylepooling in combination with global standard deviation pooling and global average pooling, is used to compress channels and preserve the representation ability of channels as much as possible for classification. The experimental results show that the proposed ESNet exhibits state-of-the-art detection performance in both spatial and JPEG domains, and achieves satisfactory robustness against the cover source mismatch.

关键词： Feature extraction transform coding Kernel Steganography Convolution Signal to noise ratio Circuits and systems Frequency-domain analysis Discrete cosine transforms Training Steganalysis deep learning two-branch preprocessing multi-level feature fusion multi-scale attention pooling

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End-to-End Optimized Image Compression With Deep Gaussian Process Regression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 3770-3785页

作者： Cao, Maida Dai, Wenrui Li, Shaohui Li, Chenglin Zou, Junni Chen, Ying Xiong, Hongkai Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China

End-to-end optimization via deep neural networks has facilitated lossy image compression. Existing neural network-based entropy models for end-to-end optimized image compression are limited by parameterized Gaussian distributions with deterministic mean and variance and cannot achieve accurate rate estimation for bottleneck representation with varying statistics. In this paper, we propose a novel entropy model based on deep Gaussian process regression (DGPR) to address this problem. Specifically, the proposed entropy model leverages autoregressive DGPR to flexibly predict the channel-wise posterior distributions of high-dimensional bottleneck representation for entropy coding. Consequently, we develop a well-established bit-rate estimation scheme via posterior inference of DGPR using the learned probabilistic distribution. Furthermore, scalable training is achieved via tensor train decomposition and Monte Carlo sampling to enable tractable variational inference of DGPR. To our best knowledge, this paper is the first attempt to develop the learnable probabilistic model for flexible parameter estimation in entropy modeling. Experimental results show that the proposed model outperforms conventional image compression methods (e.g., JPEG2000 and BPG) as well as recent end-to-end optimized methods on the Kodak and Tecnick datasets in terms of rate-distortion performance.

关键词： Image coding Entropy Context modeling Gaussian processes Computational modeling Entropy coding transform coding Deep Gaussian process regression entropy modeling image compression

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Deep Learning for Seismic Data Compression in Distributed Acoustic Sensing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Chen, Yangkang Saad, Omar M. Chen, Yunfeng Savvaidis, Alexandros Univ Texas Austin Bur Econ Geol Austin TX 78713 USA King Abdullah Univ Sci & Technol KAUST Div Phys Sci & Engn Thuwal 23955 Saudi Arabia Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang P Hangzhou 310027 Zhejiang Peoples R China

Distributed acoustic sensing (DAS) is emerging in seismic monitoring due to its ultradense spatial sampling, durability to harsh environments, and sensitivity to weak ground vibration. Compared with traditional nodal geophones that are normally sparsely distributed, DAS offers unprecedented detectability for small-magnitude earthquake events, very subtle reservoir dynamics, and other weak signals among various applications. The appealing detectability of weak signals is compromised by the terabyte-scale daily continuous record that causes prohibitive storage problems. The current solution is to save only the segmented data of interest, for example, a certain length around a target event. Here, we tackle the urgent storage problem of DAS monitoring by designing a deep learning (DL)-based compression algorithm. The compression algorithm can be split into two major components. The first part is the encoder based on the vision transformer architecture, where the input multichannel DAS dataset goes through an encoding process to output the key features from the input. The second part is the decoder, where the features are optimally combined to reconstruct the data of the original scale. The optimal network parameters are obtained via an unsupervised training process, aiming at minimizing the difference between the reconstructed and input data. In the proposed DL-based compression algorithm, only the decoder's weight parameters and extracted features from the input data through the encoder are saved on the disk, which is sufficient to reconstruct a high-fidelity dataset. The proposed compression algorithm can reach around 50 times the compression rate (CR) for a gigabyte-scale DAS dataset without unsatisfactory reconstruction performance.

关键词： Feature extraction transformers Decoding Computer vision Monitoring Data mining Image coding Band-pass filters transform coding Deep learning Compression deep learning reconstruction seismic

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Image Steganalysis Based on Dual-Path Enhancement and Fractal Downsampling

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 1-16页

作者： Fu, Tong Chen, Liquan Jiang, Yinghua Jia, Ju Fu, Zhangjie Southeast Univ Sch Cyber Sci & Engn Nanjing 210000 Peoples R China Nanjing Univ Informat Sci & Technol Coll Comp & Software Nanjing 210044 Peoples R China

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.

关键词： Feature extraction Filters transform coding Convolution Fractals transformers Training Adaptation models Accuracy Steganography Steganalysis dual-path enhancement fractal downsampling transformer

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HSDetect-Net: A Fuzzy-Based Deep Learning Steganalysis Framework to Detect Possible Hidden Data in Digital Images

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IEEE ACCESS 2025年 13卷 43013-43027页

作者： de la Croix, Ntivuguruzwa Jean Ahmad, Tohari Han, Fengling Ijtihadie, Royyana Muslim Inst Teknol Sepuluh Nopember Dept Informat Surabaya 60111 Indonesia Univ Rwanda Coll Sci & Technol African Ctr Excellence Internet Things Kigali 3900 Rwanda RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia

Recent progress in digital image steganalysis has been heavily influenced by advancements in Deep Learning (DL), particularly using Convolutional Neural Networks (CNNs). These networks have become a preferred choice for addressing complex classification challenges. Despite their success, current CNN-based approaches for steganalysis often encounter difficulties in accurately identifying concealed information, especially when dealing with complex textures generated by steganographic techniques. To overcome these issues, this study introduces HSDetect-Net, a novel CNN framework that integrates fuzzy logic to enhance detection capabilities. The proposed HSDetect-Net's architecture employs specialized small-sized convolutional kernels to extract complex details and incorporates a fuzzy layer to refine classification accuracy. Evaluations conducted on the Break Our Steganographic System Base (BOSSBase) and Break Our Watermarking System (BOWS) datasets reveal that HSDetect-Net achieves remarkable performance, with an accuracy of 99.07% and an F1 score of 99.68%. Compared to existing methods, HSDetect-Net improves the accuracy of detecting hidden data in images, making it a promising solution to the steganalysis of digital images.

关键词： Feature extraction Accuracy Digital images Kernel Steganography Convolutional neural networks Training transform coding Payloads Computer architecture Hidden data detection information security image steganalysis image forensics convolutional neural network (CNN)

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