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Diffusion-based Identity-Preserving Facial Privacy Protection

Diffusion-based Identity-Preserving Facial Privacy Protectio...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Han, Dong Mohamed, Salaheldin Li, Yong Denzler, Joachim Huawei Munich Research Center Friedrich Schiller University Jena Jena Germany Huawei Munich Research Center Munich Germany Computer Vision Group Friedrich Schiller University Jena Jena Germany

ISBN: (纸本)9798350368741

The efficacy of facial recognition systems that utilize deep learning techniques has led to significant concerns over privacy, since they possess the capability to facilitate unauthorized monitoring of individuals in the digital realm. Current techniques for improving privacy are ineffective in producing "naturalistic" photographs that can safeguard facial features and fail to ensure privacy while maintaining an optimal user experience. We present an innovative text-agnostic method for protecting facial privacy. Our method depends on manipulating the sampling process of a pretrained diffusion model utilizing the guidance from a target image face together with the original image and face guidance in an adversarial manner to produce a protected face image. We preserve the original visual information from the input face image for identity preservation while extracting general embedding information from the target face image for soft facial attribute transfer. The output protected face image from our method has imperceptible facial changes with enhanced privacy protection against state-of-the-art (SOTA) face recognition (FR) systems. Our extensive studies have shown that the faces generated using our method have a higher level of black-box adaptability, resulting in an absolute improvement of 6.4% on CelebA-HQ compared to the current most effective SOTA facial privacy protection technique in the face verification task while maintaining high image fidelity. © 2025 IEEE.

关键词： Diffusion Face Recognition Facial Privacy

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DKD2L: Dual Knowledge Distillation Dynamic Learning for sketch-based 3D shape retrieval

DKD2L: Dual Knowledge Distillation Dynamic Learning for sket...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Su, Yawen Bai, Jing Lin, Gan School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798350368741

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD2L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD2L surpasses existing state-of-the-art methods. © 2025 IEEE.

关键词： 3D Shape Retrieval Knowledge Distillation Sketch-based 3D Shape Retrieval Temporal Feature

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SPT: Sequence Prompt Transformer for Interactive Image Segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 IEEE.

关键词： computer vision interactive image segmentation

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on Computer vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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Echo signal processing Method for Ultrasonic Guided Wave Pipeline Defect Detection Based on EMD and WT 24

Echo Signal Processing Method for Ultrasonic Guided Wave Pip...

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

作者： Li, Zhonghu Li, Hao Zhao, Weilong Zhang, Xinyu Yang, Liqing Wang, Jinming School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Baotou China Shandong Special Equipment Inspection Institute Jinan China School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9798400710681

Magnetostrictive ultrasonic guided wave detection technology possesses remarkable advantages such as non-contact measurement, large lift-off distance, and high detection efficiency. It is a nondestructive testing method with broad application prospects. To improve the accuracy of defect identification and localization, it is necessary to process the ultrasonic echo signals of pipeline defects. Add a 10 dB noise signal to the collected analog raw defect echo signal, and then apply EMD, EMD-WT, and WT-EMD methods to denoise the noisy echo signal separately. The results show that when using the EMD method alone for denoising useful information in the high-frequency components is lost, and the denoising effect is not significant;Combining EMD with WT, the two-stage denoising effect is superior to denoising with EMD alone, and the WT-EMD method shows even better denoising performance. It can effectively improve the signal-to-noise ratio. © 2024 Copyright held by the owner/author(s).

关键词： signal denoising

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3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yue, Yubiao Xue, Jun Liang, Haihuang Li, Zhenzhang Wang, Yufeng School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model's ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba's parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba. © 2025 IEEE.

关键词： Deep Leraning Lightweight Model Mpox Detection vision Mamba

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yu, Hao Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (纸本)9798350368741

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS. © 2025 IEEE.

关键词： Binary-antithetical transition Domain generalization Face anti-spoofing Information bottleneck

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Long-tailed Oracle Character Recognition Based on Convolutional Neural Networks and vision Transformers

Long-tailed Oracle Character Recognition Based on Convolutio...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yang, Zhongyuan Han, Zhiwang Aysa, Alimjan Ibrahim, Ghalip Ubul, Kurban School of Computer Science and Technology Xinjiang University Xinjiang China Xinjiang Key Laboratory of Multilingual Information Technology Xinjiang University Xinjiang China

ISBN: (纸本)9798350368741

Oracle bone inscriptions, which are among the oldest known hieroglyphics in China, encompass rich historical and cultural information. However, the automatic recognition of oracle characters faces substantial challenges due to issues with data quality and long-tail distribution. This study introduces a hybrid model based on a convolutional neural network (CNN) and a visual transformer (ViT), augmented with a multi-expert learning strategy, to enhance the recognition performance of long-tail oracle bone characters. Initially, the CNN extracts local features, while the ViT captures global dependencies, thereby improving the model's feature representation capability. Subsequently, through the multi-expert learning mechanism, the prediction results of multiple models are aggregated, effectively mitigating the effects of data imbalance and alleviating the challenges associated with the long-tail distribution of the dataset. Experimental results demonstrate that the proposed model achieves new state-of-the-art performance on two large-scale Oracle datasets (Oracle-AYNU and OBC306) in terms of Top-1 accuracy, Top-5 accuracy, F1-Score, and average accuracy, with respective values of 87.97% (93.61%), 96.58% (98.84%), 86.83% (93.49%), and 82.81% (85.23%). © 2025 IEEE.

关键词： convolutional neural network long-tail distribution Oracle recognition visual transformer

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SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network for Medical Image Segmentation

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network fo...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Zhanpeng Zhang, Yuqiang Wang, Bin Yang, Yang Cai, Lin Institute of Artificial Intelligence Xiamen University Xiamen China School of Artificial Intelligence Hubei University Wuhan China School of Life Sciences Hubei University Wuhan China

ISBN: (纸本)9798350368741

Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods often face challenges such as low contrast, blurred boundaries, and unclear backgrounds in medical images. Considering that perceived objects and features exhibit greater discriminative power in the frequency domain, we propose a novel mamba-based spatial and frequency domain feature fusion network, SFMa-Unet, to address these challenges. Specifically, we designed the Spatial-Frequency Interaction (SFI) module, which leverages the powerful modeling capabilities of Mamba to fuse spatial and frequency domain features, enhancing feature representation. Additionally, we developed a Mamba-based multi-scale feature channel fusion (MFCF) bridge to capture local and global dependencies across different feature scales, further improving the model's representational capacity. We conduct comprehensive experiments on the ISIC17 and ISIC18 public datasets. Experimental results demonstrate the effectiveness and robustness of SFMa-Unet. Codes are available at https://***/RainCh-zyq/SFma-Unet. © 2025 IEEE.

关键词： Medical Image Segmentation spatial-frequency fusion vision State Space Models

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