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A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional Neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) Stochastic computing

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Research on RGB-D Visual SLAM Algorithm Based on Adaptive Target Detection

Research on RGB-D Visual SLAM Algorithm Based on Adaptive Ta...

引用

2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： Wei, Baoguo Zhao, Lina Li, Lixin Li, Xu School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350316728

Most of the traditional SLAM systems are based on static environment assumptions, but they are easily affected by dynamic targets in real environments, resulting in a serious degradation of the robustness and accuracy of the algorithms. In this paper, we focus on visual SLAM systems in dynamic scenes, introducing an object detection network in SLAM to obtain the low-level semantic information of dynamic targets, and adopting a new dynamic point selection strategy to classify the detected targets into three motion types, and then fusing the semantic information to eliminate the dynamic feature points. Experiments show that the proposed method outperforms traditional methods in dynamic scenarios, and the real-time performance of the proposed method is improved compared with the semantic segmentation-based SLAM system. © 2023 IEEE.

关键词： Object detection

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Research on UAV Aerial image Dehazing Algorithm Based on CycleGAN

Research on UAV Aerial Image Dehazing Algorithm Based on Cyc...

引用

2024 International conference on Intelligent Computing and Robotics, ICICR 2024

作者： Wang, Henan He, Yin Department of Information Systems Naval Dalian Ship Academy Liaoning Dalian China

ISBN: (纸本)9798350372274

Effective perception of the environment is a prerequisite for autonomous detection by unmanned aerial vehicles (UAVs), and accurate acquisition of visual images is the most crucial aspect. Considering that the visibility of UAV visual images is greatly affected by natural factors such as fog, and current dehazing algorithms are not very effective, this paper proposes a dehazing method for UAV images based on a cyclic generative adversarial network with an atrous spatial pyramid pooling and attention module. The unpaired image unsupervised dehazing task is performed using the constrained transfer learning ability of the CycleGAN. Considering the severe loss of edge details in actual foggy images and the complex distribution of fog, it is necessary to combine real visual features for dehazing. This paper replaces the ordinary convolution with the ASPP module and incorporates the Convolutional Block Attention Module into the network to non-uniformly process different features and areas in foggy images. Experimental results show that this method achieves better results on the foggy image dataset. ©2024 IEEE.

关键词： Generative adversarial networks

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VeXKD: The Versatile Integration of Cross-Modal Fusion and Knowledge Distillation for 3D Perception 38

VeXKD: The Versatile Integration of Cross-Modal Fusion and K...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ji, Yuzhe Chen, Yijie Yang, Liuqing Ding, Rui Yang, Meng Zheng, xinhu Guangzhou China Hong Kong University of Science and Technology Hong Kong Xi'an Jiaotong University Xi'an China

Recent advancements in 3D perception have led to a proliferation of network architectures, particularly those involving multi-modal fusion algorithms. While these fusion algorithms improve accuracy, their complexity often impedes real-time performance. This paper introduces VeXKD, an effective and Versatile framework that integrates Cross-Modal Fusion with Knowledge Distillation. VeXKD applies knowledge distillation exclusively to the Bird's Eye View (BEV) feature maps, enabling the transfer of cross-modal insights to single-modal students without additional inference time overhead. It avoids volatile components that can vary across various 3D perception tasks and student modalities, thus improving versatility. The framework adopts a modality-general cross-modal fusion module to bridge the modality gap between the multi-modal teachers and single-modal students. Furthermore, leveraging byproducts generated during fusion, our BEV query guided mask generation network identifies crucial spatial locations across different BEV feature maps from different tasks and semantic levels in a data-driven manner, significantly enhancing the effectiveness of knowledge distillation. Extensive experiments on the nuScenes dataset demonstrate notable improvements, with up to 6.9%/4.2% increase in mAP and NDS for 3D detection tasks and up to 4.3% rise in mIoU for BEV map segmentation tasks, narrowing the performance gap with multi-modal models. © 2024 Neural information processing systems foundation. All rights reserved.

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Accelerated Event-Based Feature Detection and Compression for Surveillance Video systems 24

Accelerated Event-Based Feature Detection and Compression fo...

引用

15th ACM Multimedia systems conference (ACM MMSys)

作者： Freeman, Andrew C. Mayer-Patel, Ketan Singh, Montek Univ North Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9798400704123

The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called AD Delta ER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.

关键词： event representation event video video processing event vision

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Parameter estimation of the GTD model based on performance-enhanced MUSIC algorithms 3

Parameter estimation of the GTD model based on performance-e...

引用

3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Xu, Jia-Hua Zhang, xiao-Kuan Zong, Bin-Feng Li, NingHui Lv, Fan Wang, ZhaoLong The Graduate School of Air Force Engineering University Xi’an710051 China Air and Missile Defence College Air Force Engineering University Xi’an710051 China

ISBN: (纸本)9781510670945

Scattering centers are important features of targets at high frequency regions and the geometric theory of diffraction (GTD) scattering center model is the typical one to describe scattering centers. Therefore, it is quite vital to estimate parameters of GTD model accurately. The classical multiple signal classification (MUSIC) algorithm can effectively estimate these parameters at high signal-to-noise ratios (SNR) but it suffers from poor parameter estimation performance at low SNR scenarios. To solve this problem, we propose a modified MUSIC algorithm which enhances the noise robustness. The modified MUSIC algorithm construct a new total covariance matrix R by averaging the auto-correlation matrix of the original back-scattered data and the auto-correlation matrix of its conjugate data. Then, we take even powers of R , which can broaden the differences between the eigenvalues of noises and signals and avoid overlapping spectral peaks. The theoretical computational complexities of the main modified step are discussed in this paper. Simulation results verify that the proposed algorithms achieve superior accuracy in parameter estimation of the GTD model and obtain better noise robustness. What is more, a target recognition method based on the GTD model and the artificial intelligence algorithm are proposed in this paper. Simulation results validate the effectiveness of this method. © 2023 SPIE.

关键词： Covariance matrix

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SAR image Despeckling based on Gradient Domain Convolutional Sparse Coding 15

SAR Image Despeckling based on Gradient Domain Convolutional...

引用

15th International conference on Signal processing systems, ICSPS 2023

作者： Chen, Gao Li, Zifeng Zhou, Qingfeng Liu, Chanzi Dongguan University Of Technology 1 University Rd Dongguan China

ISBN: (纸本)9781510675056

Sparse regularization is an effective tool for synthetic aperture radar (SAR) image *** effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation. © 2024 SPIE.

关键词： Synthetic aperture radar

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Real-Time Multi-Camera Video Stitching Algorithm with Adaptive Sensing of Illumination and Dynamic Objects 4

Real-Time Multi-Camera Video Stitching Algorithm with Adapti...

引用

4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Lei, Min He, Lijun Li, Fan Xi'an Jiaotong University Department of Information and Communications Engineering Xi'an China

ISBN: (纸本)9798350359985

With the development of digital image processing technology, the demand and application of wide-view video continue to grow. However, the existing stitching algorithms may result in unnatural appearance, misalignment, and ghosting issues when stitching scenes with inconsistent brightness or dynamic objects. To address the above problems, we propose a real-time multi-camera video stitching algorithm with adaptive sensing of illumination and dynamic objects. Within the proposed illumination compensation module, brightness equalization across varying illumination conditions is attained through the application of gain compensation to image blocks. We propose a histogram-based dynamic object detection approach to dynamically detect the presence of moving objects along the stitching seam. This method facilitates the adaptive detection of dynamic entities by analyzing the histogram disparities between the local vicinity of the stitching seam and the broader image context. The experimental results show that it can effectively solve the problem of unnatural stitching results arising from the uneven brightness of the image, and can effectively detect the presence of dynamic objects near the stitching seam so that the effect of the video stitching has been improved. © 2024 IEEE.

关键词： Object detection

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Performance Evaluation of Underwater image Enhancement Techniques

Performance Evaluation of Underwater Image Enhancement Techn...

引用

2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Shubhangi, Adagale-Vairagar Gupta, Praveen CSMU Navi Mumbai Mumbai India

ISBN: (纸本)9798350382693

Absorption, scattering, and colour distortion make underwater photography difficult. Marine biology, underwater archaeology, and surveillance need better underwater photos. This research compares cutting-edge underwater image-enhancing methods to assess their efficacy and usefulness. This paper provides a comparative analysis of the various filtering approaches for underwater image enhancement, such as median filtering, average filtering, Gaussian filtering, Wiener filtering, and wavelet-based soft thresholding. The performance of these techniques is evaluated based on the mean square error, peak signal-to-noise ratio, average gradient, PCQI, and UICQI. The effectiveness of the suggested filtering scheme is validated on some sample images of the public UIEB dataset. It provides the analytical finding, advantages, disadvantages and performance of the different techniques for wide variety of underwater images enhancement. The paper also explores underwater image enhancement issues and future approaches. The promising research directions are integrating domain-specific information, real-time processing, and adaptive algorithms for dynamic underwater scenes. The discoveries help increase underwater photography visibility and interpretability, increasing underwater exploration and monitoring systems. © 2024 IEEE.

关键词： Underwater imaging

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Detail enhancement for infrared image based on Iterative least squares and difference of Gaussian filter 3

Detail enhancement for infrared image based on Iterative lea...

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3rd International Computing Imaging conference, CITA 2023

作者： Chen, Zhiqiang Zhao, Zehao Guan, Lei Zhou, Feng Chen, Yaohong Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Shaanxi Xi’an710119 China University of Chinese Academy of Sciences Beijing100049 China Xi’an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology Shaanxi Xi’an710119 China

ISBN: (纸本)9781510671409

Effective visualization of infrared images with low contrast and low signal-to-noise ratio is one of the key technologies for high-performance infrared imaging systems. The conventional decomposition-based algorithms have advantages in image details enhancement, but still suffer from high computational cost, unbalanced noise suppression and detail information. In this paper, we decompose the base and detail components of the image by the iterative least squares and the Difference of Gaussian filter, and further enhance the base layer and the detail layer via plateau equalization and gradient mask, respectively. We then fusion the enhancement result and re-project to 8-bit dynamic range. Experimental results shown that the proposed method achieves a good balance between detail enhancement and computational cost, with a high-performance in different scenes. © 2023 SPIE.

关键词： image enhancement

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