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Secure and High-Fidelity Medical image Generation: A GAN-based Approach for Steganography 3

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

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3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Sumitra, B. Selvin Ebenezer, S. Vallathan, G. Sundaramurthy, B. Dorsela, Venkata Rami Reddy 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 Salem India

ISBN: (纸本)9798331527549

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. © 2025 ieee.

关键词： Generative adversarial networks

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2.5D Interposer HBM signal Integrity Analysis based on Fast MoM 9

2.5D Interposer HBM Signal Integrity Analysis Based on Fast ...

引用

9th international conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Dai, Wenliang Liu, Ping Jiang, Liguo Xpeedic Co. LTD. Shanghai China

ISBN: (纸本)9798331509453

High-bandwidth memory (HBM) placed side-by-side with ASIC on silicon interposer is capable of delivering the TB/s bandwidth. To maintain such high bandwidth, it is crucial to perform extensive signal integrity analysis and optimization. In this paper, a novel methodology for fast HBM signal integrity analysis and optimization is proposed, which includes accurate HBM channel model extraction and fast HBM channel simulation in both frequency domain and time domain with IBIS-AMI models to ultimately enable full signal coverage with automation. © 2024 ieee.

关键词： image segmentation

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Review on Key Technologies of Preprocessing in Underwater Optical image 8

Review on Key Technologies of Preprocessing in Underwater Op...

引用

8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Yin, Yue Chongqing Business Vocational College School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798350302455

The first step in the development and application of marine resources is to process the captured ocean optical images. Compared with the general image, the underwater image processing is more complex, which increases the difficulty of the development of marine resources. In order to assist the development of marine resources and the identification and analysis of marine organisms, this paper summarizes and analyzes the key technologies of underwater optical image preprocessing. The underwater image preprocessing is divided into underwater image acquisition, image clarity and image segmentation. Introduce the hardware equipment of underwater image acquisition;introduces the underwater image sharpening algorithm from three aspects: traditional color constancy theory, comprehensive technology and depth learning;introduce an image segmentation algorithm based on deep learning semantic segmentation. And enumerate and analyze various datasets used for underwater light vision technology. Through the comparison and analysis of various image preprocessing algorithms, the current situation of underwater image preprocessing technology is objectively evaluated. Discusses the problems existing in underwater image preprocessing technology and gives suggestions. Assisted in the development and application of marine resources. © 2023 ieee.

关键词： Marine biology

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Retinex by Autoencoders 16

Retinex by Autoencoders

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Pezzoni, Claudio Mio, Corrado Barsotti, Annalisa Univ Milan Dipartimento Informat Giovanni degli Antoni Via Celoria 18 I-20133 Milan Italy Khalifa Univ Sci & Technol EBTIC Abu Dhabi City U Arab Emirates Univ Milan Dipartimento Informat I-20133 Milan Italy

ISBN: (纸本)9781665464956

The Retinex algorithms find wide applications as image enhancers, for their capability of preserving edges, while at the same time attenuating smooth gradients and chromatic dominants. They are characterized by the fact that the output chromatic intensity of a pixel is not determined in isolation (or looking only at the contiguous pixels) but through an operation of comparison to different local and remote areas of the image. This local/global comparison implies also a high computational cost for the algorithms: their complexity is not linear with the number of pixels;furthermore, the more systematic the comparison, the higher the complexity. Thus, most Retinex algorithms are unfit for real-time processing. The recent development of efficient Machine Learning architectures for image Processing has raised the question of whether one of the Retinex "transforms" could be efficiently learned by training a feed-forward Artificial Neural Network, thus creating a model characterized by short processing time. Selecting a variant of the Random Spray Retinex model - FuzzyRSR - as representative of the Retinex family, and choosing suitably structured autoencoder neural networks, we found that we could accurately reproduce the Retinex effects. The computational cost of the training phase was moderate, while that of the inference phases was linear in the number of pixels, and three orders of magnitude lower than the one of FuzzyRSR, thus making the ANN implementation of Retinex suitable for real-time processing.

关键词： Autoencoders FuzzyRSR image Enhancement Random Spray Retinex Retinex

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Latent Feature Regularization based Adversarial Network for Brain Tumor Anomaly Detection

Latent Feature Regularization based Adversarial Network for ...

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ieee international conference on Multimedia and Expo (ICME)

作者： Wang, Nan Chen, Chengwei Lin, Shaohui Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665468916

Brain tumor anomaly detection plays a critical role in the field of computer-aided diagnosis, which has attracted ever-increasing focus from the medical community However, brain tumor data are scarce and tough to classify. Unsupervised methods enable the reduction of huge labeling costs to be applied to brain tumor anomaly detection during the training only given normal brain images. However, the existing unsupervised methods distinguish whether the input image is abnormal in the image space, which cannot effectively learn the discriminative features. In this paper, we propose a novel brain tumor anomaly detection method via Latent Feature Regularization based Adversarial Network (LFRA-Net), which leverages a latent feature regularizer into adversarial learning to obtain the discriminative features. Comprehensive experiments on BraTS, HCP, MNIST, and CIFAR-10 datasets evaluate the effectiveness of our LFRANet, which outperforms state-of-the-art unsupervised learning methods.

关键词： Anomaly detection multi-model medical image latent feature regularization adversarial learning

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Prompt Fusion and Aspect-Oriented Filtration for Aspect-based Multimodal Sentiment Analysis

Prompt Fusion and Aspect-Oriented Filtration for Aspect-Base...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Sun, Jiachang Li, Xiuhong Zhu, Fuxian School of Computer Science and Technology XinJiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830046 China School of Economics and Management XinJiang University Urumqi830046 China

ISBN: (纸本)9798350368741

Currently, aspect-based multimodal sentiment analysis (ABMSA) remains a highly hot research field, aiming to leverage various modalities such as images and text to determine the sentiment orientation of viewpoint entities. Although deep learning methods have made significant progress in this field, some challenges still exist: image information unrelated to the viewpoint entity can introduce noise, insufficient interaction during modal fusion. To solve these problems, we propose a novel ABMSA model with prompt fusion and noise filtration. Specifically, we introduced an image Noise Filtering (INF) module that can filter the extracted image features to reduce the noise introduced by the irrelevant between viewpoint entity and the image. Additionally, we implemented Multimodal Prompt Fusion (MPF) module to enhance the interaction between modalities, addressing the issue of minimal effective features related to viewpoint entity during the fusion stage of text and image features. Experimental results show that the model outperforms the baseline model on two public datasets, Twitter-2015 and Twitter-2017, demonstrating that our proposed approach effectively enhances the accuracy of aspect-based multimodal sentiment analysis tasks. © 2025 ieee.

关键词： Aspect-based Sentiment Analysis Multimodal Sentiment Analysis Noise Filtration Prompt Fusion

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FPGA implementation of AIQ based Daubechies filter Bank for Medical image Retrieval 1

FPGA implementation of AIQ based Daubechies filter Bank for ...

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1st ieee international conference on Advances in signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Samantaray, Aswini Kumar Sahoo, Satyajit Rahulkar, Amol D. Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Dept. of Electronics and Communication Engineering Manipal India Vfstr Dept. of Electronics and Communication Engineering Guntur India Nit Dept. of Electrical and Electronics Engineering Goa India

ISBN: (纸本)9798350355093

Wavelet transform techniques are crucial to image processing, particularly in medical imaging, where the need for precise feature extraction is critical. This article describes the design and FPGA implementation of an Algebraic Integer Quantization (AIQ) based Daubechies-6 (DAUB6) wavelet filter bank, required for efficient medical image retrieval. The proposed AIQ-based orthogonal wavelet filter bank helps to increase the retrieval accuracy by optimizing the frequency and spatial resolution, important for medical images. The current filter bank design is executed on a Field Programmable Gate Array (FPGA) using Xilinx VirtexE FPGA board to leverage its parallel processing capabilities, ensuring low power consumption. The FPGA implementation is validated using a dataset of medical images such as NEMA, OASIS and EXACT09. Through extensive simulations and hardware synthesis, the proposed system demonstrates significant improvements in retrieval efficiency and accuracy compared to existing wavelet filter banks. © 2024 ieee.

关键词： Wavelet transforms

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A 3D Attenuation Coefficient based Degradation Estimation for Real Non-Homogeneous Dehazing

A 3D Attenuation Coefficient based Degradation Estimation fo...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Jiang, Hanyu Ren, Zemin Tan, Tao Chongqing University of Science and Technology China Co. Ltd. China University of Science and Technology of China China

ISBN: (纸本)9798350368741

The end-to-end image dehazing network relies on paired training data. However, there is limited training data available for real non-homogeneous dehazing, which limits the performance of dehazing networks on real non-homogeneous hazy images. To overcome this limitation, we propose a method to augment the training datasets for real non-homogeneous image dehazing. Different with existing methods that introduce augment data from other datasets, we estimate the degradations and synthesize additional hazy images by applying it to other scenes in the same dataset. During the estimation, we proposed a new degradation model based on 3D attenuation coefficient for describing non-homogeneous degradation. To solve the 3D attenuation coefficient, we propose a clue kernel to overcome the scene-dependence of the estimated degradation. The experimental results show that our proposed method can effectively augment the training dataset and improve the performance and robustness of the dehazing network. Compared to state-of-the-art methods, our approach outperforms the best performing method by 4.65% SSIM on the Dense-NH-HAZE dataset. © 2025 ieee.

关键词： data augmentation degradation model image dehazing

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A Review of Privacy-Preserving Federated Learning, Deep Learning, and Machine Learning IIoT and IoTs Solutions 8

A Review of Privacy-Preserving Federated Learning, Deep Lear...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Obarafor, Victor Qi, Man Zhang, Leishi Canterbury Christ Church University United Kingdom

ISBN: (纸本)9798350397932

internet of Things (IoT) is a growing computing trend that encompasses every connected thing. Over the recent years, IoT has recorded an exponential growth, leading to billions of smart devices, and still increasing. In contrast to other computing devices, some IoTs generate large amount of data, however, this has become a source of concern as data could contain users' privacy which should be protected at all costs against any potential security breach incident. Securing IoT is very significant with its continuous adoption and use, hence, researchers have proposed several security mechanisms and techniques to safeguard and protect IoT systems and devices. Notwithstanding, there are some research gaps that are yet to be addressed irrespective of the relevant contributions made in protection of users' privacy and confidentiality using IoTs. In this paper, the researcher solely focused on a review of AI approaches leveraged by researchers in protecting the device and data security aspects of privacy specifically for de-centralised architecture based industrial IoT systems (IIOTs) as they are generating large amount of data and are safety critical. The results achieved, unresolved issues and recommendations for future research are contained in this review. © 2023 ieee.

关键词： internet of things

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SAMNER: image Screening and Cross-Modal Alignment Networks for Multimodal Named Entity Recognition

SAMNER: Image Screening and Cross-Modal Alignment Networks f...

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international Joint conference on Neural Networks (IJCNN)

作者： Yang, Luyi Xinjiang Univ Sch Comp Sci & Technol Urumqi Xinjiang Peoples R China Xinjiang Key Lab Signal Detect & Proc Urumqi Xinjiang Peoples R China

ISBN: (纸本)9798350359329;9798350359312

With the proliferation of social media data, Multimodal Named Entity Recognition (MNER) has received much attention;using different data modalities is crucial for the development of natural language processing and neural networks. However, existing methods suffer from two drawbacks: 1) textimage pairs in the data only sometimes correspond to each other, and it is impossible to rely on contextual information due to the short text nature of social media. 2) Despite the introduction of visual information, heterogeneity gaps may occur in previous complex fusion methods, leading to misidentification. This paper proposes a new synthetic image with a selected graphic alignment network(SAMNER) to address these challenges and construct a matching relationship between external images and text. To solve the graphic mismatch problem, we use a stable diffusion model to generate the images and perform entity labeling. Specifically, we generate images and perform entity labeling through the stable diffusion model to generate the image with the highest match to the text, filter the generated images by the internal image set to generate the best image, and then perform multimodal fusion to predict the entity labeling, we design a simple and effective multimodal attentional alignment mechanism to obtain a better visual representation, and we conduct a large number of experiments. The experiments prove that our model produces competitive results on the two publicly available datasets.

关键词： Named entity recognition Natural language processing Social media

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