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Rethinking Camouflaged Object Detection via Foreground-Background Interactive Learning

Rethinking Camouflaged Object Detection via Foreground-Backg...

引用

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

作者： Zhang, Chenxi Zhang, Qing Wu, Jiayun Faculty of Intelligence Technology Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350368741

Camouflaged object detection focuses on the challenge of segmenting objects that visually blend into their background. The effectiveness of camouflage strategies hinges on how well objects interact with their background to minimize their visibility. based on this insight, we propose a novel Foreground-Background Interactive Learning Network (FBINet), which independently decouples foreground and background information, and facilitates bi-directional interactions. This allows the network to progressively complement each other, leading to high-quality predictions with clear boundaries. To the best of our knowledge, this is the first attempt to tackle the camouflaged object detection task through interactive learning between foreground and background, which not only better reveals camouflage patterns but also offers a new perspective in this field. Experimental results on three datasets demonstrate that our proposed FBINet outperforms current state-of-the-art methods in performance while maintaining a low computational cost, making it applicable for real-world scenarios. The code will be available at https://***/bbdjj/FBINet. © 2025 ieee.

关键词： Camouflaged object detection foreground-background interaction image segmentation

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A Semantic Communication Method for Intelligent internet of Things based on images 6

A Semantic Communication Method for Intelligent Internet of ...

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6th international conference on Electronics technology, ICET 2023

作者： Wan, Zhiping Liu, Shaojiang Xu, Zhiming Zou, Jiajun Li, Huizhen Guangzhou Xinhua University School of Information and Intelligence Engineering Guangzhou China

ISBN: (纸本)9798350337693

For image processing tasks in intelligent internet of Things (IoT) devices, the traditional method is to upload the raw image data of IoT devices to an edge/cloud server for processing, and then return the processed results to the IoT end. However, this method may cause significant delay problems and affect the real-time performance of IoT devices and application effects for large data tasks. To solve this problem, this paper proposes an image semantic communication method that reduces the amount of data transmitted by using a semantic encoder to extract key category information and spatial arrangement information from the original image. At the receiving end, key information is regenerated through semantic deconstruction and reconstruction methods using a decoder, so that the receiving end can use the generated image data to perform tasks normally. Simulation results show that this method outperforms traditional communication methods in terms of improving latency performance. © 2023 ieee.

关键词： Semantics

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MetaLeak: Assessing image Metadata Leakage in Android Apps 21

MetaLeak: Assessing Image Metadata Leakage in Android Apps

引用

2024 ieee/ACS 21st international conference on Computer systems and Applications, AICCSA 2024

作者： Nguyen, Tran Thanh Lam Carminati, Barbara Ferrari, Elena University of Insubria Varese Italy

ISBN: (纸本)9798331518240

Although modern smartphone platforms emphasize user privacy protection by continually improving security mechanisms, vulnerabilities still exist, especially in the case of the Android operating system. The Android security mechanism almost delegates the entire responsibility of granting access permissions to apps to end users, who often are unaware of all the possible consequences of granting permission. Additionally, the loose protection mechanism regulating access to media files (images, videos, audio, etc.) can be exploited by attackers as a side-channel to gather sensitive data. This paper shows how sharing images containing sensitive metadata may result in an intentional or unintentional leakage of users' personal or confidential information. We designed MetaLeak, a system based on apps' hybrid analysis, to assess the identified risks. We used MetaLeak to analyze 5,000 popular apps and found that 21.9% of them sent at least one type of sensitive metadata over the internet. Moreover, for only 10.4% of the apps in our dataset, the app's actual behavior w.r.t. collecting GPS data is compliant with the developer's claims. © 2024 ieee.

关键词： Information leakage

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

引用

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

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 ieee.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Making Adversarial Attack Imperceptible in Frequency Domain: A Watermark-based Framework

Making Adversarial Attack Imperceptible in Frequency Domain:...

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ieee international conference on Multimedia and Expo (ICME)

作者： Zhang, Hanxiu Cao, Guitao Zhang, Xinyue Xiang, Jing Wu, Chunwei Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China East China Normal Univ MoE Engn Res Ctr SW HW Co Design Technol & Applicat Shanghai 200062 Peoples R China

ISBN: (纸本)9781665468916

With the development of multimedia communication technology, the image information stored in electronic devices faces increasing privacy risks and requires processing for protection. However, it is found that adversarial perturbations added to images for semantic information protection may corrupt the frequency domain watermarks added for copyright statement. With such challenges, we propose an Adversarial Frequency domain Watermarking (AFW) framework to protect images from both copyright and semantic content. Specifically, the AFW framework constructs the images as adversarial examples by embedding crafted adversarial watermarks in the frequency domain, followed by an optimization algorithm to improve the visual quality. Notably, AFW can generally integrate with existing watermark and attack methods. Extensive experiments on five network models and the imageNet dataset demonstrate that the AFW framework can achieve information hiding and adversarial attacking goals under visual quality assurance.

关键词： Digital watermark Adversarial example Frequency domain image security

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG signal with Correlation-based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 ieee international conference on Computing, Applications and systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 ieee.

关键词： Deep neural networks

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Effective Breast Cancer Classification Using SDNN based E-Health Care Services Framework 10

Effective Breast Cancer Classification Using SDNN Based E-He...

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10th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET-SIP-22 2022

作者： Vaka, Anji Reddy Soni, Badal Murugan, R National Institute of Technology Department of CSE Assam Silchar India

ISBN: (纸本)9781665467414

In the modern environment, many health care issues are raised day by day. Detecting the early stage of breast cancer may lead to prevention. In this paper, we proposed the breast cancer classification of the E-health care services framework with the internet of Medical Things by utilizing the support value-based Deep Neural Network (SDNN) classification. Initially, the input cytology images are taken from the local health care center, and then attain the process of pre-processing and filtering technique is used the noise removed image goes to the feature extraction process includes entropy, Geometrical features, Textural features. After that segmentation is done using Histo-sigmoid fuzzy clustering. Finally, it attains the classification process of the proposed SDNN support value-based deep neural network. SDNN classifier classifies the breast cancer images as normal or abnormal compared with the existing approaches. The proposed method accuracy is 97.4 % which is better than other state - of - the art methods. © 2022 ieee.

关键词： Deep learning Support vector machines Measurement image segmentation Neural networks internet of Medical Things Information processing

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An Application of Quantum Mechanics to Attention Methods in Computer Vision 48

An Application of Quantum Mechanics to Attention Methods in ...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Zhang, Juntao Luo, Yihao Cheng, Peng Li, Zehan Wu, Hao Yu, Kun An, Wenbo Zhou, Jun Ams Institute of System Engineering Beijing China Yichang Testing Technique R&d Institute Yichang China Coolanyp L.L.C. Wuxi China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728163277

This work proposes the quantum-state-based mapping (QSM) for machine learning. QSM uses wave functions that describe microscopic particle systems as mappings. By QSM, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. QSM has a low computational cost, almost no additional parameters, and is easy to integrate with other modules. We demonstrate the simplest form of the wave function as a mapping, that is, when a one-dimensional particle is in an infinitely deep potential well, in combination with advanced attention modules. Experiments show that QSM significantly improves the feature recalibration ability of attention module in transfer learning tasks. Then, we tried to analyze the effectiveness of QSM. This work indicates that QSM has an important application value in interdisciplinary machine learning. © 2023 ieee.

关键词： Attention method image classification Quantum mechanics Transfer Learning

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Investigation on BPSK Modulated LoRaWAN for Data Rate Enhancement 3

Investigation on BPSK Modulated LoRaWAN for Data Rate Enhanc...

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3rd ieee international conference on Applied Electromagnetics, signal Processing, and Communication, AESPC 2023

作者： Divyasree, K. Manoj, G. Mansi, A. Sindhukavi, S. Ramesh, Parameswaran Bino, J. Shabana Parveen, M. Bhuvaneswari, P.T.V. Anna University Department of Electronics Engineering Madras Institute of Technology Chennai India St Josephs's Institute of Technology Anna University Department of Electronics and Communication Engineering Chennai India Sri Sairam Engineering College Anna University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350358742

The internet of Things (IoT) is an upcoming and promising technology that involves efficient transmission techniques to communicate data obtained from sensory devices in the developed application. The role played by the channel characteristics between these devices is significant, as quality of service metrics are vital factors in determining the performance of the application. To support long-distance communication, LoRaWAN technology is being used in most of the IoT-enabled automation systems. It provides a wider coverage area and encodes the information using Chirp Spread Spectrum (CSS). A low data transmission rate is found to be its major limitation. This research investigates the bit error rate (BER) behaviour of LoRa when the sensed data are modulated with BPSK under different signal-to-noise ratio (SNR). The impact of the spreading factor on data rate is also analyzed. From the MATLAB simulation results, it is found that an increase in the spreading factor reduces the data rate and vice versa. Hence, to obtain a better data rate, a lesser spreading factor is suggested. © 2023 ieee.

关键词： internet of things

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international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

International Conference on Electrical, Computer, and Energy...

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4th ieee international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

ISBN: (纸本)9798350395914

The proceedings contain 353 papers. The topics discussed include: statistical approach of anaphoric resolution for computational linguistics using rule based system in text summarization;reliability-based design optimization for OPGW cable damper systems;a robust security method for medical image based on 2D Schaffer map;a mathematical model of asymmetric encryption based on linear Diophantine equations;leveraging vehicle predictive analytics through adaptive learning and cloud-aided sensor fusion;the work on the application of electrical machines and the project method in a public university of Peru;on digital art generation using generative adversarial networks;evaluation of blind signal separation objective function constructed using nonlinear functions;CPH white-box cartoon GAN: a patch-based style-swap approach;and unified structure to generate sparsity-inducing regularizers for enhanced low-rank matrix completion.

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