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Multi image partition steganography method based on phase multiplexing technology 2

Multi image partition steganography method based on phase mu...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Niu, Hongwei Mao, Zhewen Wang, Yan Co. Ltd. Shanghai China Co. Ltd. Shanghai China

ISBN: (纸本)9781510689053

In the Internet environment, facing the growing demand for information capacity and image security transmission, the traditional single image encryption transmission is no longer sufficient. This paper proposes multi-image segmentation steganography, based on phase multiplexing, using the principle of spatial angle interferometry and random phase multiplexing digital holography. The information of several images is encrypted into one gray image, which not only ensures the security of transmission, but also increases the capacity of system information steganography. Using correlation coefficient and PSNR as evaluation criteria for similarity and noise interference, as an evaluation criterion for similarity and noise interference, and conducting experiments under simulated conditions, the results confirm that there is not much difference between the steganographic carrier and the original image, the pixel grayscale uniformity has strong randomness. This paper demonstrates that this method can effectively encrypt multiple images simultaneously, with simple calculation, security and reliability, strong noise resistance, and is significantly superior to other methods. This study proposes a multi-image segmentation steganography method that uses phase multiplexing to encrypt multiple images into a single grayscale image, significantly improving information capacity while ensuring secure transmission. Experimental results show that the steganographic image exhibits minimal differences from the original images, with strong randomness and noise resistance. However, the method's performance in highly noisy environments can be further improved. Future work should focus on optimizing the algorithm to enhance stability in complex conditions and addressing issues of compatibility and synchronization in multi-channel transmissions. © 2025 SPIE.

关键词： image segmentation

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image Processing Based Wood Defect Detection 2nd

Image Processing Based Wood Defect Detection

引用

2nd International conference on Information Technologies and Their Applications

作者： Ozkan, Merve Ozcan, Caner Karabuk Univ Inst Postgrad Educ Comp Engn Karabuk Turkiye Karabuk Univ Software Engn Fac Engn Karabuk Turkiye

ISBN: (纸本)9783031734199;9783031734205

Detection of defects in wooden structures in the forestry industry has become a crucial area of research. Existing studies have focused on specific categories of wood defects, failing to provide a comprehensive classification for high-quality wood. Trained human operators currently perform a variety of wood quality in wood processing facilities. However, this human-dependent process leads to time and performance losses and inaccurate type. This study aims to address all these challenges in future intelligent production systems by targeting the detection of the fungus in oak wood, one of the wood defect classes. The algorithm created based on image processing utilizes median filtering, Canny edge detection, and masking technologies using the HSV color space. The algorithm then calculates the fungal area ratio to the wooden piece's surface area on the masked image to reach the final result. While existing studies in the literature are primarily based on deep learning methods, there has been limited focus on fungus detection. The novelty of this study, conducted on oak wood, lies in its use of a specific dataset, fungal detection, and image processing. An algorithm has been developed and presented in the literature that can be used in the software of future intelligent production systems in the forestry industry.

关键词： image Processing Wood Material Object Detection Canny Edge Detection HSV

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data Augmentation Method for Small-Sample Strip Steel Surface Defects Based on Improved ACGAN 24

Data Augmentation Method for Small-Sample Strip Steel Surfac...

引用

2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wang, Chengzhi Lv, Donghao Yang, Chunlong Tian, Xu Inner Mongolia University of Science & Technology Inner Mongolia Autonomous Region China

ISBN: (纸本)9798400710681

This study introduces a novel data augmentation method aimed at addressing the limited availability of sample data for strip steel surface defects, utilizing an improved Auxiliary Classifier Generative Adversarial Network (ACGAN). Firstly, on the generator side, the improved ACGAN integrates an encoder that extracts essential features from images of strip steel surface defects and transforms them into latent vectors. These vectors serve as inputs for the generator, helping it learn the actual data distribution of strip steel defects more accurately. To enhance image resolution and reduce checkerboard artifacts common with transposed convolution, the generator uses a combination of transposed convolution and PixelShuffle for upsampling. Secondly, on the discriminator side, the convolutional network employs an alternating configuration of downsampling and standard convolutional layers, effectively reducing feature dimensions while preserving more local information. A Dropout regularization strategy is applied after each convolutional layer to boost the model’s robustness. Finally, the stability of the learning process is reinforced by employing Kaiming initialization for both the generator and discriminator’s convolutional and transposed convolution layers. Performance evaluations conducted using the Northeastern University surface defect database confirm the efficacy of the proposed method in generating accurate images of strip steel surface defects in small-sample scenarios. Notably, compared to the original ACGAN and other classical generative adversarial networks, the improved method demonstrated significant improvements, achieving reductions of 52.82% and 38.78% in FID and MMD metrics, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Auxiliary classifier generative adversarial network

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image Interpolation Utilizing Neighboring Pixels as References in Conjunction with Multilayer Perceptron Neural Networks 2nd

Image Interpolation Utilizing Neighboring Pixels as Referenc...

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Lu, Tzu Chuen Vo, Thanh Nhan Jana, Biswapati Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science Vidyasagar University Midnapore721102 India

ISBN: (纸本)9789819777853

This study seeks to investigate the utilization of neural networks for the generation of high-quality image interpolation. This introduction endeavors to deliver a comprehensive exploration of the foundational principles underpinning interpolation, coupled with an investigation into the substantial potential presented by neural networks in this field. We introduce an image interpolation approach and substantiate its exceptional performance through empirical evidence. Additionally, we develop methodologies for evaluating experimental interpolation outcomes and explore potential applications in other domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interpolation

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LANet: Lightweight Attention Network for Medical image Segmentation 2nd

LANet: Lightweight Attention Network for Medical Image Segme...

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2nd International conference on Information Technologies and Their Applications

作者： Tang, Yi Pertsau, Dmitry Zhao, Di Kupryianava, Dziana Tatur, Mikhail Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9783031734199;9783031734205

Medical image segmentation plays a crucial role in quantitative analysis, clinical diagnosis and medical interventions. However, extracting valuable information from medical images is challenging due to the presence of different object types and scales, complex backgrounds and tissue similarities. To address these issues, LANet, a Lightweight Attention Network which incorporates an Efficient Fusion Attention (EFA) block and an Adaptive Feature Fusion (AFF) decoding block was presented in the paper. The EFA block enhances the model's feature extraction capability by capturing task-relevant information while reducing redundancy in channel and spatial locations. The AFF decoding block fuses the purified low-level features from the encoder with the sampled features from the decoder, enhancing the network's understanding and expression of input features. Additionally, the model adopts MobileViT as a lightweight backbone network with a small number of parameters, facilitating easy training and faster predictive inference. The efficiency of LANet was evaluated using four public datasets: kvasir-SEG, CVC-clinicDB, CVC-colondB, and the data Science Bowl 2018. Experimental results demonstrate that LANet outperforms state-of-the-art methods in terms of intersection over union (mIoU) and Dice scores.

关键词： LANet Lightweight Attention Network Efficient Fusion Attention Block Adaptive Feature Fusion Decoding Block Attention Mechanism

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2D cluster finding with the help of enhanced deep residual networks 2

2D cluster finding with the help of enhanced deep residual n...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xia, Taixi Zhang, Xin Xu, Zheming Department of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Mathematics Jinan University Guangdong Guangzhou511443 China Department of Mathematics and Physics University of Science and Technology Beijing Beijing100000 China

ISBN: (纸本)9781510689053

In recent years, advancements in image processing technology have made cluster detection a vital area of research across various scientific fields, from particle physics to astronomy. Accurately identifying clusters in images is essential for enhancing experimental outcomes and applications. In this paper, a novel algorithm for two-dimensional cluster finding using Enhanced Deep Residual Network (EDSR) is proposed. The algorithm solves the challenge of accurately identifying points in an image by processing the pixel matrix to efficiently locate clusters. The algorithm uses a three-step process: identifying local maxima, clustering around these points, and calculating a weighted average to determine the cluster center. While traditional methods have difficulty in solving the problem of equality of adjacent maxima, our method solves this problem by redefining local maximum detection to minimize bias. By considering a 5 × 5 matrix around each local maximum, clustering was achieved, capturing over 98.73% of the peak values and ensuring comprehensive clustering formation without overlapping issues. Before clustering analysis, the EDSR algorithm is used to improve image resolution and reduce errors in identifying cluster centers. The model evaluation results indicate that the accuracy of cluster center determination has been improved and the error margin has been reduced, highlighting the effectiveness of the EDSR enhancement method. © 2025 SPIE.

关键词： Noise abatement

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Computer Vision and Deep Learning-Based Model for Detecting Spoofed Faces in images 2nd

Computer Vision and Deep Learning-Based Model for Detectin...

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2nd International conference on data Science and Network Engineering, ICDSNE 2024

作者： Salian, Gayathri P Rao, Manasa K Rashmi, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819783359

In the modern era of smart applications, video data is critically important in various contexts. In most of these applications, cameras are frequently incorporated to facilitate authentication. As a result, face recognition is the biometric method most frequently employed to authenticate users in these applications. The vulnerability of face recognition systems to spoofing attacks grows in tandem with their increased usage. As a result, robust countermeasures are required. This paper presents an approach to face anti-spoofing through transfer learning and YOLOv8 optimization. Additionally, a custom dataset was constructed using images obtained from web cameras and an existing dataset to assess the proposed work’s real-time effectiveness. The proposed approach also adds a blurriness threshold during image capture to improve performance. With a mean Average Precision (mAP50) of 0.975, the experimental outcomes highlight the model’s effectiveness in detecting face spoofing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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data augmentation method for rice appearance quality inspection based on improved ACGAN 2

Data augmentation method for rice appearance quality inspect...

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2nd International conference on the Frontiers of Intelligent Control, Measurement, and Signal Systems, FICMSS 2025

作者： Long, Tao Zhang, Lichen Liu, Yi Zeng, Detian School of Information and Electrical Engineering Hunan International Economics University Changsha410205 China Information School Hunan University of Humanities Science and Technology Loudi417000 China

There is a shortage of labeled data in the non-destructive testing task of rice appearance quality, which leads to low detection accuracy. To solve the aforementioned problem, this article proposes a rice appearance image generation technique based on improved ACGAN to enhance the accuracy of detection tasks. By utilizing feature fusion and residual connections from multiple feature maps, the ACGAN structure is improved to enhance the quality of generating different types of rice images. Additionally, a dataset augmentation scheme for defect rice detection scenarios is proposed. The experiment shows that the data generation technology proposed in this paper performs well in generating image tasks, with an average SSIM and PSNR score improvement of 9.9% and 2.0%, respectively, compared to the second place. The data augmentation scheme proposed in this article also achieved the best performance in non-destructive testing of rice appearance quality, significantly outperforming other comparative methods. In brief, this article provides new ideas and methods for non-destructive testing of rice appearance quality, which has good social and economic value. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Nondestructive examination

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

2nd European Symposium on Artificial Intelligence in Manufac...

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2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024

ISBN: (纸本)9783031864889

The proceedings contain 29 papers. The special focus in this conference is on Artificial Intelligence in Manufacturing. The topics include: On the Use of Generative AI to Support In-Line Process Monitoring in Zero-Defect Manufacturing;Laser Metal Deposition (LMD) Process Monitoring: from 3D Visualization of Sensor data Towards Anomaly Detection;an Explainable Active Learning Approach for Enhanced Defect Detection in Manufacturing;Synthetic data for AI-Powered Ultrafast Laser Based Micro-structuring Method Description;Towards Explicable AI in Systemic Identification of Surrogate Models of Manufacturing Processes;Leveraging Generative AI for Synthetic data Generation: Improving 6-DOF Pose Estimation in Assembly Systems;CPPM Copilot: Proposing an AI-Based Assistant for Manual Assembly Tasks in a Flexible Production;generalized Authoring Tool for Computer Vision Machine Learning Application Deployments;collaborative Learning in Shared Production Environment Using Federated image Classification;An OPIS-Based Knowledge Engineering Framework for Collaborative Robotics;a Tool for Generating and Labelling Domain Randomised Synthetic images for Object Recognition in Manufacturing;human-Robot Interaction Through Egocentric Hand Gesture Recognition;advanced Computer Vision for Industrial Safety: Indoor Human Worker Localization Using Deep Learning;LOS data Set: A Large Scale Online Scheduling Benchmark for Flexible Job Shop Problems with Setup and Transportation Times;agent-Based Communication for Fault Diagnosis in Skill-Based Production Environments Using Messages Based on I4.0 Language and Asset Administration Shells;Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling;AI Based Solutions for Manufacturing Mass Customization;Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR);On an AI-Based System Architecture to Generate Robotic Cells in VR Environments;Building on the Principles of LLM

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Generative image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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