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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain

Medical Image Authentication and Self-Recovery Using Fragile...

引用

international image Processing, Applications and systems conference (IPAS)

作者： Riadh Bouarroudj Fouzi Harrou Nabil Zerrouki Feryel Souami Fatma Zohra Bellala Ying Sun Department of Computer Science University of Sciences and Technology Houari Boumedienne Algiers Algeria CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia DI2M Laboratory Center for Development of Advanced Technology Algiers Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers Algeria

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%.

关键词： Location awareness Accuracy image color analysis Authentication Watermarking Transforms Gray-scale Discrete wavelet transforms Cryptography Biomedical imaging

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6G-Enabled Attack Detection System for IoT-Driven Smart City Using Machine Learning

6G-Enabled Attack Detection System for IoT-Driven Smart City...

引用

Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Anjali Rajak Shashi Tiwari Prince Rajak Rakesh Tripathi Department of Information Technology National Institute of Technology Raipur Raipur India Department of ECE International Institute of Information Technology Naya Raipur Raipur India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Rapid urbanization has led to the emergence of smart cities, where interconnected devices and technologies aim to improve urban management. A smart city utilizes internet of Things (IoT) sensors in urban environments to collect data and automate the systems. However, the exponential growth of IoT devices presents significant challenges, including inadequate security high latency, and limited bandwidth. The advent of sixth-generation (6G) wireless communication technology, marks a paradigm shift in addressing these challenges. In this study, a 6G-enabled Attack Detection System (6G-IoTSCADS) for IoT-driven smart cities is introduced. The proposed system utilizes the feature ranking and selection method to obtain an optimized feature set, which is then fed into the attack detection model i.e. eXtreme Gradient Boosting (XGBoost). The proposed system is evaluated using an AIoT-SOL IoT-based dataset. The results demonstrate the superiority of the proposed system over other machine-learning techniques.

关键词： 6G mobile communication Wireless communication Technological innovation Smart cities image edge detection Feature extraction Sensor systems internet of Things Security Intelligent sensors

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Processing Continuous-Valued signals for Multimodal Spike-based Pose Regression

Processing Continuous-Valued Signals for Multimodal Spike-Ba...

引用

2025 IEEE international conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates Sorbonne University Dept. Science and Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331516857

In underwater missions, energy-efficient operation is critical to maximize the operability of an autonomous robots and mission duration. At the same time, accurate pose estimation at reduced computational cost is essential for various underwater robotics and exploration applications. This paper presents an energy-efficient model for pose regression tasks based on direct Spike-Coding-Decoding (SCD) with adaptive threshold mechanism. The proposed approach enables efficient processing of multimodal continuous-valued images and IMU data. The effectiveness of the proposed module is tested using an open-sourced underwater simulator. In underwater robotics domain, real-world testing remains critical due to limited access to actual underwater environment, making simulators invaluable for rapid algorithm development and refinement. Accordingly, HoloOcean simulator is utilized to collect images and IMU sensor data from various underwater scenarios, which are then fed into a pose estimation framework. The SCD module uses 2D and 1D convolutional layers followed by LIF neurons with adaptive threshold to convert continuous-valued data into sparse spike-based signals. During spike decoding, the spike-based signals are converted back to continuous-values by utilizing the accumulated membrane potential of the output LIF neurons. The feasibility of SCD module is evaluated by its ability to introduce sparsity in spike-based representations with minimal information loss that enables efficient signal reconstruction. Custom multimodal data is collected using the HoloOcean simulator and is used to train the network. We compared the performance of the adaptive learnable threshold method with fixed threshold methods. The experimental results show that spike coding with an adaptive threshold mechanism is highly effective compared to a fixed threshold mechanism, as it can encode the continuous-valued input to its spike-based representation with ≈ 2× higher sparsity and a lesser spike rate. ©

关键词： image reconstruction

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Optimization Techniques in Electronics: Advances, Challenges, and Future Directions

Optimization Techniques in Electronics: Advances, Challenges...

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international conference on ent Computing and Control systems (ICICCS)

作者： Gaurav Mishra Khushi Kumari Chethan Sharma Dept of CSE M.S. Ramaiah Institute of Technology Bangalore Dept of CSE(AI & ML) M.S. Ramaiah Institute of Technology Bangalore Dept of CSE(AI & ML) and Dept of CSE (Cyber Security) M.S. Ramaiah Institute of Technology Bangalore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Optimization techniques support the solutions of contemporary electronic challenges by managing resources while improving energy conservation and boosting performance together with scalability capabilities. The rapid technological progress of Very Large-Scale Integration (VLSI) together with internet of Things (IoT) and computer networks and signal processing and communication systems requires improved optimization techniques. Research focuses on few selected studies from 2023 to 2025 to conduct an extensive review of optimization methods used in these technological fields. The review groups selected studies according to their application sectors and their methodologies as well as their findings while noting important advantages and constraints along with practical results. The objective of this work is to provide a comprehensive analysis of existing optimization strategies, identifying their strengths and limitations to guide future advancements. The review discusses future optimization directions based on the study of emerging trends which include AI optimization principles and quantum computing capabilities and multi-objective optimization approaches. Research has identified major gaps in scalability and computational complexity along with the need for real-world testing and energy efficiency studies for electronic system optimization. Future work recommendations target these gaps to improve electronic system optimization.

关键词： Quantum computing Reviews Scalability Very large scale integration signal processing Energy efficiency Computer networks internet of Things Computational complexity Optimization

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6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Completion

6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Comple...

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Digital signal Processing and its Applications (DSPA), international conference on

作者： Semyon Dorokhin Vladimir Lyashev Mikhail Makurin Moscow Institute of Physics and Technology Moscow Russia Radio Transmission Technology Lab Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Channel estimation overhead reduction is one of the main problems for 6G XL-MIMO systems. As the number of antennas and subcarriers grows, so does the overhead of traditional channel estimation methods. High overhead limits user mobility and affects the latency. Most of the popular tensor-based channel estimation algorithms combine reference signals transmission and channel tensor estimation into one problem. Such approach typically imposes limitations on reference signals, making backward-compatibility with existing 5G standard challenging. We propose to separate tensor completion and channel tensor elements estimation into different tasks. We show that tensor completion algorithms from image processing and fMRI scanning can be reused for sub-Nyquist completion of OFDM MIMO tensors to reduce the overhead. Furthermore, we extend one of these algorithms from CPD to Tensor Train (TT) and demonstrate that TT-based algorithm reduces channel estimation error up to 2 times. With the proposed approach other algorithms can be further developed based on tensor completion theory.

关键词： 6G mobile communication Tensors OFDM image processing Channel estimation signal processing algorithms Estimation Massive MIMO Functional magnetic resonance imaging Standards

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Multi-Factor Prediction of Welding Deformation of Pump Truck Arm based on Hybrid Deep Learning Model 2

Multi-Factor Prediction of Welding Deformation of Pump Truck...

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2nd international conference on the Frontiers of Intelligent Control, Measurement, and signal systems, FICMSS 2025

作者： Cao, Shen Liu, Zehui Chen, Weitao Hunan International Economics University Changsha410205 China College of Mechanical and Electrical Engineering Shihezi University Shihezi832003 China

During the welding process of the pump truck arm, due to the influence of multiple factors such as materials, processes, and structures, deformation is often difficult to control, affecting the welding quality and subsequent processing accuracy. Therefore, how to accurately predict welding deformation and formulate effective control measures has become a difficult problem that needs to be solved in the engineering field. To this end, this paper analyzes the multi-factor characteristics of pump truck arm welding deformation, identifies the main factors affecting deformation, including welding heat input, cooling rate, material properties and process parameters, and this paper uses a combination of CNN and LSTM networks to predict welding. First, CNN is used to extract features from image data of the welding process. Then LSTM is used to capture dynamic changes in time series data. Finally, the advantages of these two models are combined to optimize prediction through an integrated learning method. Specifically, the mean squared error (MSE) of the hybrid model is 0.023, and the root mean squared error (RMSE) is 0.151, which is significantly better than the DNN and GRU models. In addition, the model maintains high prediction accuracy under noise interference and has good real-time response capability. In the above data conclusions, the hybrid deep learning model based on CNN and LSTM can effectively handle the multiple factors affecting the welding process, provide more accurate and stable welding deformation prediction results, and provide strong support for the precise control and quality improvement of the pump truck arm welding process. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Deep learning

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Secure and High-Fidelity Medical image Generation: A GAN-based Approach for Steganography

Secure and High-Fidelity Medical Image Generation: A GAN-Bas...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： B. Sumitra S. Selvin Ebenezer G. Vallathan B. Sundaramurthy Venkata Rami Reddy Dorsela Department of ECE Geethanjali College of Engineering & Technology Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Vinayaka Missions Research Foundation Vinayaka Missions Kirupananda Variyar Engineering College (Deemed to be University) Salem India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Traditional image steganography methods embed secret data into cover images but often face detection by steganalysis tools due to detectable pixel modifications, which increase the risk of data leakage. These techniques also typically degrade image quality and offer limited capacity. To overcome these challenges, a novel approach for medical imaging is proposed, integrating Generative Adversarial Networks (GANs), PatchGAN, Adaptive LSB steganography, and a dual attention mechanism. PatchGAN improves fine-grained texture details, while Adaptive LSB securely embeds sensitive data within the images. The dual attention mechanism highlights critical regions, enhancing both image clarity and diagnostic accuracy. This method improves image quality and security, benefiting clinical decision-making and patient outcomes. The system is evaluated against traditional methods like GAN and IDGAN using metrics such as embedding capacity, classification accuracy (stego, cover, global), and discriminator loss. Results demonstrate that the proposed system outperforms existing techniques, offering significant improvements in both performance and security for medical imaging applications.

关键词： image quality Steganography Attention mechanisms Adaptive systems Accuracy image synthesis Generative adversarial networks Security Data communication Medical diagnostic imaging

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High-Frequency Vision-based Tactile Sensor with Embedded Transparent Piezoelectric Module for Humanoid Robotic Perception

High-Frequency Vision-Based Tactile Sensor with Embedded Tra...

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IEEE international conference on Micro Electro Mechanical systems

作者： Zhengyi Xie Chunpeng Jiang Haoxiang Jiang Yimeng Sun Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Zhangjiang Laboratory China Shanghai University of Electric Power China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper reports a novel composite high-frequency vision-based tactile sensor with an embedded transparent MEMS-based piezoelectric module. Taking advantage of the transparency of the piezoelectric module, the sensor can simultaneously capture high-quality image signals and piezoelectric signals. By combining high-frequency piezoelectric signals with the interpolation algorithm, the sensor enhances the frame rate of image signals, enabling the acquisition of high-frequency, high-quality image signals. Additionally, this integrated approach offers advantages in terms of miniaturization and low cost. This research provides a new direction for the development of humanoid robotic perception and has broad application potential.

关键词： Micromechanical devices Human computer interaction Interpolation Visualization image resolution Heuristic algorithms Multimodal sensors Tactile sensors Humanoid robots signal resolution

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Detection and Classification of Monkeypox Disease with Chatbot

Detection and Classification of Monkeypox Disease with Chatb...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： P. Chandu P. Siddardha Priyadharshini K Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Monkeypox detection is essential for effective public health management and controlling its spread. Timely detection enables early intervention in outbreaks, reducing transmission risk. This project presents an innovative framework that enhances monkeypox detection and classification using deep learning and an interactive chatbot. It employs the VGG16 convolutional neural network (CNN) for accurate classification of monkeypox from medical images, utilizing performance metrics like precision, recall, and F1 score for reliability. Additionally, the system includes a neural network-based chatbot that provides interactive assistance, answers user questions, offers symptom information, and guides users through the diagnostic process. This chatbot improves user engagement and accessibility, linking clinical diagnostics with patient interaction. Built on the Flask framework, the system ensures a smooth, user-friendly interface for both image classification and chatbot functionalities.

关键词： Deep learning Measurement Neural networks Chatbots Real-time systems Convolutional neural networks Reliability Public healthcare Medical diagnostic imaging image classification

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image Compressive Sensing With Adaptive Sampling by Median Filtering

Image Compressive Sensing With Adaptive Sampling by Median F...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Yanfeng Wu Chen Hui Ronghua Liao Shaohui Liu Debin Zhao Department of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.

关键词： Adaptive systems Filtering signal processing algorithms signal processing Transformers Iterative algorithms Sensors Speech processing Compressed sensing image reconstruction

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