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DiGradPatch: Black-Box Patch Attacks via Diffusion-Based Double Gradient and Sensitive Distribution Guidance

DiGradPatch: Black-Box Patch Attacks via Diffusion-Based Dou...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yang Wu Jing Liu College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks have demonstrated vulnerabilities to black-box adversarial patch attacks in image analysis tasks, raising concerns about their robustness in safety-critical applications. Current methods typically rely on randomized search strategies to determine patch locations and apply unrestricted pixel perturbations in the patch area, leading to high query costs and significant visual distortions that reduce imperceptibility. To address these limitations, we propose Black-Box Patch Attacks via Diffusion-Based Double Gradient and Sensitive Distribution Guidance (DiGradPatch), a novel method designed to generate highly imperceptible adversarial samples with minimal query cost. Our approach leverages the prior sensitive distribution of a surrogate model to efficiently locate the patch by maximizing the joint probability distribution between the global model prediction and selected image regions. Furthermore, we introduce the diffusion model into the black-box patch attack framework for the first time. The prior gradient from the diffusion model, in conjunction with the estimated gradient of the target model, is used to guide the direction of perturbations. This allows the generation of adversarial perturbations within the patch area that align with the distribution of natural samples, effectively reducing perceptual distortions. Extensive experiments on the imageNet dataset show that DiGradPatch achieves superior imperceptibility with significantly reduced query costs, maintaining a high attack success rate. Compared to existing methods, our approach requires fewer than 10 queries and achieves an L ∞ norm that is only 1/10 of state-of-the-art techniques, while maintaining the highest attack success rate.

关键词： Visualization Costs Acoustic distortion Perturbation methods Closed box Predictive models Diffusion models Search problems Probability distribution Speech processing

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Towards Green VAE: A Light Pixel-weighting Technique to Enhance Variational AutoEncoder

Towards Green VAE: A Light Pixel-weighting Technique to Enha...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Cheng Zhong Junlin Wu Ziming Feng Boan Chen Junchi Yan Dept. of CSE MOE Key Lab of AI Credit Card Center China Merchants Bank School of AI Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Variational autoencoders (VAEs) has been a popular generative model for its effectiveness, mathematical foundation, and its impact to other approaches in deep generative learning. For its relatively light-weights and easiness for training, compared with Generative Adversarial Networks (GANs) or other large-scale model e.g. Diffusion, VAEs become a viable tool in cost-efficient generative applications especially considering the so-called green AI. However, in comparison to GANs, the performance of VAEs in generating realistic images is still inferior to the state-of-the-art generative adversarial network (GAN). In this paper, we argue that this problem is at least partly due to the irrational reconstruction in VAE that all pixels are equally weighted, which is harmful to the generating ability (or density estimate). Motivated by this, we propose to compute the weights of pixels. First, we formulate the problem of finding the appropriate weights into an optimal problem, and then give an analytical solution. Moreover, we propose a method to apply the computed weights of pixels into the training pipeline with almost no computation overhead which fits with the spirit of green AI for more energy-saving AI especially for deep learning models. Experiments on MNIST, Fashion-MNIST and CIFAR-10 show that our method can significantly improves VAE in terms of FID.

关键词： Training Deep learning Autoencoders Pipelines Speech enhancement signal processing Generative adversarial networks Mathematical models Artificial intelligence image reconstruction

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Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine images

Multi-Task Joint 3D Swin Transformer Learning for Segmentati...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Dong Zhang Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Institute of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convolutional kernel may restrict the receptive field and inadequately capture the sequential properties of the data. Meanwhile, due to the large data volume of hyperspectral images, current medical hyperspectral images focus more on individual segmentation or classification. This paper proposed a 3D swin transformer with multi-task joint learning framework, to simultaneously learn multiple tasks for hyperspectral tongue images. Based on the 3D swin transformer model, the framework regards cross-band context feature learning of hyperspectral images as a sequence-to-sequence prediction process. The 3D transformer encoder used as the basic framework for shared feature extraction, and set up corresponding decoders for prediction according to different visual tasks such as segmentation and classification. We conducted simultaneous segmentation and classification tasks of the tongue coating region on the hyperspectral image of tongue images. The results showed that the proposed model had good results in single tasks of segmentation and classification, and performed better than other multi-task convolutional neural network models.

关键词： Training image segmentation Visualization Tongue Three-dimensional displays Transformers Feature extraction Multitasking Hyperspectral imaging Biomedical imaging

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G-Depth: An Efficient Graph Method for Robust Depth Completion

G-Depth: An Efficient Graph Method for Robust Depth Completi...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhongyu Huang Yijun Chen Aizierjiang Aiersilan Li Li University of Macau Macau SAR China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Depth completion has played a vital role in enhancing depth perception in various applications such as autonomous driving, robotics, and 3D reconstruction. It is a critical task that involves generating a dense depth map from an RGB image and an aligned sparse depth map. However, many existing studies simply assume the depth map to be readily available, overlooking the fact that depth maps are often missing or subject to perturbations in real-world scenarios. The absence of these data can mislead the network into overfitting incorrect patterns, ultimately diminishing the performance of depth completion models. To tackle these challenges, we propose G-Depth, a method that innovatively involves Graph Neural Networks (GNNs) into a two-branch backbone. By integrating the graph convolutional mechanism into the deep layers of the network, G-Depth achieves an expanded receptive field, enabling a more accurate understanding and capture of spatial relationships between objects. To enhance robustness, we also propose a novel Enhanced Data Distillation(EDD) method to address the issue of modality absence, mitigating its negative effects, achieving a 73.2% average improvement in RMSE without sparse depth input. Comprehensive experiments on KITTI dataset demonstrate the effectiveness and robustness of the proposed G-Depth.

关键词： Accuracy Three-dimensional displays Convolution Perturbation methods Computer architecture Robustness Real-time systems Speech processing Optimization Overfitting

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Imperceptible Transfer Attack on Large Vision-Language Models

Imperceptible Transfer Attack on Large Vision-Language Model...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： xiaowen Cai Daizong Liu Runwei Guan Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In spite of achieving significant progress in recent years, Large Vision-Language Models (LVLMs) are proven to be vulnerable to adversarial examples. Therefore, there is an urgent need for an effective adversarial attack to identify the deficiencies of LVLMs in security-sensitive applications. However, existing LVLM attackers generally optimize adversarial samples against a specific textual prompt with a certain LVLM model, tending to overfit the target prompt/network and hardly remain malicious once they are transferred to attack a different prompt/model. To this end, in this paper, we propose a novel Imperceptible Transfer Attack (ITA) against LVLMs to generate prompt/model-agnostic adversarial samples to enhance such adversarial transferability while further improving the imperceptibility. Specifically, we learn to apply appropriate visual transformations on image inputs to create diverse input patterns by selecting the optimal combination of operations from a pool of candidates, consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as an adversarial learning problem and employ a gradient approximation strategy with noise budget constraints to effectively generate imperceptible transferable samples. Extensive experiments on three LVLM models and two widely used datasets with three tasks demonstrate the superior performance of our ITA.

关键词： Visualization Noise Adversarial machine learning Acoustics Speech processing

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FCoDT-Net: A Novel Framework for High-Precision Medical image Segmentation Using Contextual Distillation Transformer

FCoDT-Net: A Novel Framework for High-Precision Medical Imag...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qin YuTao Yang SiZhe Hu Bang Ren Wei School of Computer and Information Science Southwest University Chongqing China Chongqing Fu Dan Digital Medical Technology Co. Ltd Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current methods in medical image semantic segmentation often rely on simple skip connections within U-shaped network structures. These approaches fail to bridge the semantic gap between the encoder and decoder module and do not fully exploit the rich contextual information among. The unused information leads to suboptimal segmentation results. In this paper, we propose the Feature Context Distillation Transformer Network (FCoDT-Net), a deep learning model designed to address these limitations by leveraging the rich contextual information within the skip connections. FCoDT-Net introduces a novel Context Distillation Transformer (CoDT) within its decoder module, which effectively utilizes abundant contextual information from skip connections. CoDT leverages attention matrix calculations between feature maps at similar scales to exploit rich context information. The narrowed semantic gap then can generate features which can be more efficiently utilized by the decoder. Further, we integrate Convolutional Network Next(ConvNext) block into our encoder module to enhance feature extraction capabilities. Experimental simulations show that our FCoDT-Net excels in both 3D and 2D applications. In 3D, FCoDT-Net achieves exceptional results on the lung dataset, with a Dice score of 0.9250 for airway segmentation. In 2D, FCoDT-Net demonstrates state-of-the-art performance on both the Gland Segmentation (Glas) and Multi-Organ Nuclei Segmentation and Classification (MoNuSeg) datasets.

关键词： Solid modeling Three-dimensional displays Accuracy Semantic segmentation Semantics Glands Transformers Decoding Speech processing Medical diagnostic imaging

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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuhan Jiang Zhongyuan Wang Jie Hua Jinbi Liang Zengmin xu National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10,724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company’s recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection; 2) show the detection results of benchmark methods; and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes.

关键词： YOLO image segmentation Accuracy Surveillance Loading signal processing algorithms Production Detectors Cameras Recycling

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PhysID: Physics-based Interactive Dynamics from a Single-view image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Sourabh Vasant Gothe Ayon Chattopadhyay Gunturi Venkata Sai Phani Kiran Pratik Vibhav Agarwal Jayesh Rajkumar Vachhani Sourav Ghosh Parameswaranath VM Barath Raj KR Samsung R&D Institute India Bangalore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness.

关键词： Performance evaluation Solid modeling Three-dimensional displays Streaming media Aerodynamics Rendering (computer graphics) Real-time systems Mobile handsets Mesh generation Material properties

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LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement

LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Re...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoyin Yan Jie Zhang Cunhang Fan Yeping Zhou Peiqi Liu NERC-SLIP University of Science and Technology of China (USTC) Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei China China Merchants Bank (CMB) Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech enhancement (SE) aims to extract the clean waveform from noise-contaminated measurements to improve the speech quality and intelligibility. Although learning-based methods can perform much better than traditional counterparts, the large computational complexity and model size heavily limit the deployment on latency-sensitive and low-resource edge devices. In this work, we propose a lightweight SE network (LiSenNet) for real-time applications. We design sub-band downsampling and upsampling blocks and a dual-path recurrent module to capture band-aware features and time-frequency patterns, respectively. A noise detector is developed to detect noisy regions in order to perform SE adaptively and save computational costs. Compared to recent higher-resource-dependent baseline models, the proposed LiSenNet can achieve a competitive performance with only 37k parameters (half of the state-of-the-art model) and 56M multiply-accumulate (MAC) operations per second.

关键词： Performance evaluation Adaptation models Time-frequency analysis Computational modeling image edge detection Noise Detectors Speech enhancement Real-time systems Noise measurement

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A Comprehensive Review of Hybrid V2x Communication and Edge processing Techniques for Real-Time Emergency Vehicle Detection and Prioritization in Urban Traffic Systems

A Comprehensive Review of Hybrid V2X Communication and Edge ...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Jerim Justin Lokkeshkumar R Vijayasri P Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This review paper presents the use of hybrid Vehicle-to-Everything (V2x) communication systems to detect and prioritize emergency vehicles in congested urban areas. To reduce response times, researchers are merging edge processing with Dedicated Short-Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2x). The technology provides extremely fast, low-latency connections, which are essential for real-time applications, by utilizing recent developments in 5G and edge computing. To increase detection accuracy in noisy urban settings, researchers are experimenting with multi-sensor fusion systems that combine GPS, acoustic sensors, and AI-powered vision. Adaptive traffic signal preemption algorithms that dynamically modify traffic flows to grant priority access to emergency vehicles are also being investigated in some research. Despite its promise, there are still obstacles to be addressed, such as interoperability problems and network congestion. However, the benefits might be enormous: quicker emergency reactions, more seamless traffic patterns, and generally more effective urban transportation systems. In the upcoming years, we should witness some fascinating breakthroughs as researchers continue to hone these technologies.

关键词： Accuracy Reviews 5G mobile communication image edge detection Urban areas Vehicular ad hoc networks Real-time systems Dedicated short range communication Vehicle-to-everything Edge computing

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