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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Drug-Target Interaction Prediction via Multiple Output Graph Convolutional Networks 17th

Drug-Target Interaction Prediction via Multiple Output Graph...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Ye, Qing Zhang, Xiaolong Lin, Xiaoli Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845315

Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. Currently, graph convolutional networks (GCNs) have been gained a lot of momentum, as its performance on non-Euclidean data. Although drugs and targets are two typical non-Euclidean data, many problems are also existed when use GCNs in DTI prediction. Firstly, most state-of-the-art GCN models are prone to the vanishing gradient problem, which is more serious in DTI prediction, as the number of interactions is limit. Secondly, a suitable graph is hardly defined for GCN in DTI prediction, as the relationship between the samples is not explicitly provided. To overcome the above problems, in this paper, a multiple output graph convolutional network (MOGCN) based DTI prediction method is designed. MOGCN enhances its learning ability with many new designed auxiliary classifier layers and a new designed graph calculation method. Many auxiliary classifier layers can increase the gradient signal that gets propagated back, utilize multi-level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The graph calculation method can use both the label information and the manifold. The conducted experiments validate the effectiveness of our MOGCN. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Object Detection for Connected Autonomous Vehicles

Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Objec...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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De Novo Drug Design via Multi-Label Learning and Adversarial Autoencoder

De Novo Drug Design via Multi-Label Learning and Adversarial...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ye, Qing Zhang, Xiaolong Lin, Xiaoli Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665401265

generating new molecules is very important for drug design. Currently, many deep generative models have been designed, such as variational autoencoder (VAE), adversarial autoencoder (AAE), and reinforcement learning (RL). However, many problems are also existed in these models. Firstly, the information among molecules could be not utilized in optimizing these models. Secondly, some useful molecule information could be not used, such as fingerprint. Thirdly, the information contained in different molecule representations could be not used together. To overcome the above problems, in this paper, a multi-label learning and adversarial autoencoder (MLAAE) based de novo drug design method is designed. MLAAE enhances its learning ability with a new designed multi-label classifier and a new designed double AAEs collaborative optimization framework. These new designs can learn a better latent space whose global distribution is similar with the random distribution, but local distribution contains much information. As a result, the generator can be trained by more information, and the input of generator in testing is similar with that in training. The conducted experiments validate the effectiveness of our MLAAE. © 2021 IEEE.

关键词： Molecules

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Computational Intelligence and AI-FML Experience Model for Pre-university Student Learning and Practice 18th

Computational Intelligence and AI-FML Experience Model for P...

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18th International Conference on Intelligent information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Lee, Chang-Shing Wang, Mei-Hui Chang, Rin-Pin Liu, Hsiao-Chi Chiu, Szu-Chi Chang, Yu-Cheng Lin, Lu-An Chen, Shen-Chien Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan KWS Center National University of Tainan Tainan Taiwan St. Dominic Catholic High School Kaohsiung Taiwan Taipei First Girls High School Taipei Taiwan Z-System Technology Kaohsiung Taiwan

ISBN: (纸本)9789819901043

This paper presents the computational intelligence (CI) and artificial intelligence (AI)–fuzzy markup language (CI&AI-FML) learning model, called the CI&AI-FML human and machine co-learning model, for pre-university students to experience and practice CI&AI-FML. Since 2019, we have engaged much in interacting and working with teachers to teach pre-university students the basic concepts of computational intelligence, such as fuzzy sets and evolutionary computing in real-world applications. To inspire and encourage more pre-university students to attend these CI&AI-FML learning and experience activities, we organized a CI&AI-FML Machine Learning Competition @ IEEE WCCI 2022 and a workshop on AI-FML Robotic Learning with AIoT (AI of Things) Applications @ 2022 IEEE CIS Summer School in Taiwan. In these events, we prepare the key but simple concepts of the CI for pre-university students learning and practice, for example, learners can practice AI-FML learning tools with Augmented Creativity (AR) and Google Teachable Machine to travel recommendation applications. From the experimental results, this kind of learning model is suitable for pre-university students to experience and practice CI for real-world applications. In future, we hope to encourage more young students and researchers to work with CI techniques and methods for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Fine-Tuning Large Language Models for Sentiment Classification of AI-Related Tweets

Fine-Tuning Large Language Models for Sentiment Classificati...

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IEEE International WIE Conference on Electrical and computer Engineering (WIECON-ECE)

作者： Md Jahid Alam Riad Rahul Debnath Mahfuzur Rahman Shuvo Fateha Jannat Ayrin Nobanul Hasan Ayesha Akhter Tamanna Prosenjit Roy Master Of Science In Information Technology (MSIT) Washington University of Science and Technology Department of Computer Science Prairie View A & M University Texas United States Department of Management Information System International American University Los Angeles California Department of Computer Science and Engineering Chittagong University (CU) Department of CSSE University Of AIUB

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Fine-tuning large language models (LLMs) for sentiment analysis presents a promising avenue for enhancing the understanding of public sentiment surrounding artificial intelligence (AI) topics. This study explores the fine-tuning of Large Language Models (LLMs) for sentiment analysis on AI-related topics using social media data. We collected approximately 20,000 tweets containing hashtags and comments related to AI concerns, including ‘AI Danger,’ ‘AI Ethics,’ ‘AI Problems,’ ‘AI Creating Problem on Jobs,’ and ‘AI Threat.’ The dataset captures public sentiment surrounding potential negative impacts of artificial intelligence. Three cutting-edge LLMs, Llama 2, Gemma, Mistral, and Phi 3, served as our foundational models for fine-tuning. To enhance model performance, we implemented advanced prompt engineering techniques, including Chain-of-Thought (COT), Algorithm-of-Thoughts (AOT), Fewshot and EmotionPrompt. Our results demonstrate that Llama 2 achieves consistent performance across all techniques with accuracies ranging from 82.3% to 83.9%. Phi-3 showed the best performance with EmotionPrompt and Fewshot techniques, achieving accuracies of 87.8% and 87.4% respectively. Gemma 7B performed optimally with Fewshot at 84.0% accuracy, while Mistral achieved 85.0% accuracy using the same technique. The research reveals that Fewshot and AOT consistently produce reliable results across models, with Emotion-Prompt showing model-specific effectiveness. Our findings have applications for enhancing the precision and dependability of sentiment analysis instruments that track public opinion and AI-related speech.

关键词： Sentiment analysis Ethics Accuracy Social networking (online) Large language models Computational modeling Instruments Speech enhancement Reliability Prompt engineering

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Design of Concurrent Engineering systems for Global Product Development Using Artificial Intelligence 10th

Design of Concurrent Engineering Systems for Global Product ...

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10th International Conference on Innovations in computer Science and Engineering, ICICSE 2022

作者： Karn, Arodh Lal Mehbodniya, Abolfazl Webber, Julian L. Jayagopal, Vellingiri Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait City Kuwait Department of Software and System Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Competition and concurrent engineering (CE) businesses are growing. Faster product creation, quality improvement, manufacturing process adoption, and reduced client demand cost are vital for corporate success. Implement concurrent engineering to make high-quality goods. CE is a parallel, systematic method to design. The chief executive officer (CEO’s) approach impacted how many firms build products. You may use the fundamental ideas of concurrent engineering conversation and the failure mode and effects analysis (FMEA) tool. To provide a smooth and efficient CE environment, conduct the product development process from market research to detailed design. Concurrent engineering is a novel product development (NPD) approach. Concurrency and cross-functional integration separate CE from traditional product development. NPD continues through developing ideas, validation, proof-of-concept validation, Minimum Viable Product development, pre-launch, and post-launch. In other words, product discovery should continue throughout its life span. Each organization's growth operations operate simultaneously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product development

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Spectral characteristics analysis of typical crops in different growth periods based on spectral domain interpolation 9

Spectral characteristics analysis of typical crops in differ...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Li, Xusheng Shao, Yakui Jiang, Chenchen Zhao, Yingjun Qin, Kai Zhang, Donghui Liu, Yufeng National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology Beijing Research Institute of Uranium Geology Beijing100029 China Precision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing100083 China Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing100871 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Computer and Information Engineering Chuzhou University Anhui 239000 China

ISBN: (纸本)9781510664432

Under the influence of the physicochemical characteristics of the crops and the space-time environmental factors, even the same crop will show some oscillation in the spectrum. Previous studies mostly used arithmetic mean value to reduce the uncertainty caused by spectral oscillation, but the characterization ability of mean value is susceptible to the degree of numerical difference. To solve these problems and to explore the relationship between the accuracy of typical crop identification and the growth period and spectral characteristics, we proposed a spectral construction algorithm based on spectral domain interpolation (SDI). Using SDI and traditional arithmetic mean (AM) method, the characteristic spectra of typical crops (winter wheat, oilseed grape) and the main background vegetation (trees, grasses, rice stubble) were constructed at different growth stages (March, April and May). Then, feature parameters were extracted based on the constructed characteristic spectrum. The importance evaluation and linear discriminant analysis of the extracted feature parameters were carried out at last. The optimal identifying growth period and identifying feature parameters of typical crops were obtained through comparative analysis, at the same time, the availability and superiority of SDI were verified. The following conclusions were drawn: (1) SDI has a good resistance to extremes, and can retain the spectral characteristics of crops well, and construct a more characterizing characteristic spectrum. (2) The best identifying growth periods of oilseed grape and winter wheat are early flowering period in March and heading period in April, respectively. The best identifying characteristics of winter wheat and oilseed grape are yellow edge position and red edge amplitude, respectively. (3) Winter wheat and oilseed grape can be well identified by using the position of the yellow edge in March and the blue edge area and the red edge amplitude in April. © 2023 SPIE.

关键词： Crops

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A Multi-Source Domain Generalization Method for Bearing RUL Prediction Based on Time-Variant and Time-Invariant Feature Extraction

A Multi-Source Domain Generalization Method for Bearing RUL ...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Zhengyu Deng Zhen Xu Juan Xu Xing Chen Qile Ren School of Computer and Information Hefei University of Technology Hefei China State Key Laboratory of High-end Compressor and System Technology Hefei China China Valve Holding (Group) Co. Ltd Shanghai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Deep learning-based remaining useful life (RUL) prediction methods have demonstrated significant advantages due to their powerful feature representation capabilities. Although existing learning models have made progress in distinguishing between the time-varying and time-invariant characteristics of vibration signals, challenges remain in fully capturing these aspects, which can hinder their generalization performance. To address this issue, we propose a domain generalization method based on the extraction of time-varying and time-invariant features, tailored to the characteristics of bearing vibration time series signals. Firstly, Fourier transform and inverse Fourier transform are employed to isolate high-frequency features, obtaining both time-varying and time-invariant signals. The time-varying signals are then processed using an attention mechanism to extract time-varying features and learn the bearing degradation trend. Concurrently, the time-invariant signals are fed into an encoder-decoder structure to extract their invariant features. Finally, both sets of features are input into a Gate Recurrent Unit (GRU) module for RUL prediction. Experimental results across three tasks demonstrate that our model achieves excellent generalization capabilities.

关键词： Vibrations Training Degradation Accuracy Frequency-domain analysis Time series analysis Predictive models Logic gates Feature extraction Market research

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