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Invisible Backdoor Attack with Image Contours Triggers 24th

Invisible Backdoor Attack with Image Contours Triggers

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Hou, Yangzhi Yang, Ying Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615445

In recent years, backdoor attack techniques on neural networks have been widely studied and researched. In this attack mode, the model implanted with a backdoor behaves normally when processing normal inputs, but once it encounters a special trigger designed by the attacker, its prediction results will be maliciously manipulated. Different backdoor attack strategies have been proposed to improve the stealth and robustness of the triggers. However, most of the existing backdoor attack strategies use fixed triggering patterns or input-independent triggering patterns, and thus are easily detected and blocked by existing defense systems. To address these limitations, we propose a backdoor attack method based on image contours. Specifically, we first use image contours as the target region for backdoor injection and fill them with specific color information to generate trigger patterns. Then, using our designed Generative Network (GN) and the Auxiliary Extraction Network (AE), the trigger patterns are covertly embedded into the images to be contaminated to generate a poisoned dataset, which contaminates the training model for the purpose of malicious attacks. Compared with traditional methods, our trigger patterns are more natural and harder to detect. Experimental results show that our proposed attack method achieves excellent performance in terms of both stealth and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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COVER: A Heuristic Greedy Adversarial Attack on Prompt-Based Learning in Language Models 20th

COVER: A Heuristic Greedy Adversarial Attack on Prompt-Base...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Tan, Zihao Chen, Qingliang Zhu, Wenbin Huang, Yongjian Department of Computer Science Jinan University Guangzhou510632 China Guangzhou Xuanyuan Research Institute Co. Ltd. Guangzhou510006 China

ISBN: (纸本)9789819970216

Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead the predictions of language models, causing serious security concerns. In this paper, we will shed light on some vulnerabilities of PLMs, by proposing a prompt-based adversarial attack on manual templates in black box scenarios. First of all, we design character-level and word-level heuristic approaches to break manual templates separately. Then we present a greedy algorithm for the attack based on the above heuristic destructive approaches. Finally, we evaluate our approach with the classification tasks on three variants of BERT series models and eight datasets. And comprehensive experimental results justify the effectiveness of our approach in terms of attack success rate and attack speed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Learning systems

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Adaptive Threshold-Driven Semi-Supervised Facial Expression Recognition 21st

Adaptive Threshold-Driven Semi-Supervised Facial Expression ...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Yiqi Zhu, Aiqing Yuan, Junbin Xu, Qingzhen School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9789819601219

Semi-supervised learning (SSL) methods are renowned for their capacity to utilise unlabelled data. In most SSLs, the threshold settings are fixed. They ignored the fact that learning difficulty varies among different categories. Moreover, many SSLs only learn from high-confidence data, overlooking the valuable facial expression features present in low-confidence data. As a consequence, the utilisation of data is limited and incomplete. To address the above issues, we propose the Adaptive Threshold-Driven Semi-Supervised (ATD-SS) method. Through adaptively adjusting thresholds, our method can make full use of the data. ATD-SS involves three parts: 1) Adaptive Adjustment Threshold Module. This module adjusts the confidence threshold by the predicted probability distribution of the labelled data. 2) Pseudo-labelling Module. For high-confidence data, this module generates pseudo labels to constrain the image prediction through the loss function. 3) Contrastive Learning Module. For low-confidence data, the similarity between features is learnt through contrastive clustering. The module is able to pull together features that belong to the same facial expression category. Experiments show that our semi-supervised method outperforms existing FER approaches and even the fully-supervised baseline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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OPECE: Optimal Placement of Edge Servers in Cloud Environment 18th

OPECE: Optimal Placement of Edge Servers in Cloud Environm...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Tao Chen, Fengmei Xue, Shengjun Li, Zheng Tian, Yachong Cheng, Xianyi School of Computer Science and Technology Silicon Lake College Suzhou215332 China School of Computer Secience Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819998951

Cloud computing offloads user tasks to remote cloud servers, which can effectively enhance the user’s network experience, but in recent years, as the number of offloaded tasks increases and users’ real-time requirements improve, cloud services are becoming increasingly challenging to meet users’ needs. Edge computing deploys multiple edge servers around the users. The shorter distance from users can significantly reduce the transmission time of task data and avoid unpredictable network latency, which is especially suitable for normal users whose tasks are mainly data-intensive tasks. However, the large variability in the density of users in different areas and the type of computing tasks (i.e., compute-intensive and data-intensive) in the same area leads to the challenge of optimally deploying multiple edge servers. To address this challenge, we design a method named optimal placement of edge servers in the cloud environment (OPECE). First, this optimal placement problem is modeled as a constrained multi-objective optimization model with task time and server utilization as the two optimization objectives. Then, this multi-objective optimization model is optimized using the Non-dominated Sorting Differential Evolution (NSDE) algorithm. Finally, the effectiveness and superiority of OPECE are demonstrated by comparing it with the currently used methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Semantic Network Analysis of a Learning Task Among Japanese Students of Psychology 29th

Semantic Network Analysis of a Learning Task Among Japanese ...

引用

29th International Conference on Collaboration Technologies and Social Computing (CollabTech)

作者： Xanat, Vargas Meza Shigen, Shimojo Hayashi, Yugo Kyoto Univ Inst Adv Study Human Biol Yoshida Konoe ChoSakyoku Kyoto 6068303 Japan Ritsumeikan Univ Grad Sch Human Sci 2-150 Iwakura Cho Osaka Ibaraki 5678570 Japan Ritsumeikan Univ Dept Comprehens Psychol 2-150 Iwakura Cho Osaka Ibaraki 5678570 Japan

ISBN: (纸本)9783031421402;9783031421419

The complexity of the learning process includes cognitive elements that are difficult to visualize in real time. Collaborative learning adds a social factor. In this study, we examined the case of Japanese university students in a psychology course who first worked individually and then in pairs to draw concept maps using a computer program. We focused on the Interactive Constructive Active/Passive (ICAP) framework of cognitive engagement through semantic and network analysis of the concept maps drawn by the students and their conversations. We drew network graphs to visualize the ICAP indicators across performance groups, uncovering that High Performers employed a wider diversity of nouns, keywords and connections related to the learning task than Low Performers. High Performers were more proactive and emotionally involved in the learning tasks. We confirmed that positive cognitive features are related to positive learning outcomes, providing recommendations for computer-supported collaborative learning (CSCL) systems according to students' needs.

关键词： computer-supported collaborative learning ICAP semantic networks

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LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking 27th

LTNER: Large Language Model Tagging for Named Entity Recogni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yan, Faren Yu, Peng Chen, Xin Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031784941

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to supervised learning methods. In our research, we developed a NER processing framework called LTNER (Code is available at https://***/YFR718/LTNER) that incorporates a revolutionary Contextualized Entity Marking Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context learning that does not require additional training, we significantly improved the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset increased from the initial 85.9% to 91.9%, approaching the performance of supervised fine-tuning. This outcome has led to a deeper understanding of the potential of LLMs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Embedding Data in Bitcoin Transaction Values with Restricted Boltzmann Machine 2nd

Embedding Data in Bitcoin Transaction Values with Restrict...

引用

2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： An, Jiayi Tian, Haibo Guangdong Province Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819785391

Constructing covert channels on blockchains has recently become a significant research focus. A major challenge lies in embedding data into blockchain transactions, while maintaining strong concealment. This study focuses on utilizing blockchain transaction values as a carrier, exploring methods to ensure that the distribution of values with embedded data mimics that of standard transactions. Initially, we gathered authentic blockchain transaction data, conducted a frequency analysis on the transaction values, and filtered out abnormal values to establish a baseline normal value dataset. Subsequently, we employed a Restricted Boltzmann Machine (RBM) to generate a simulated value dataset that mirrors the distribution of the normal dataset, facilitating the embedding of data within the simulated value dataset. By fine-tuning the number of bits embedded in the values, we achieved a value dataset with embedded data that closely resembles the normal distribution. We have calculated the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test between the value dataset with embedded data and the normal value dataset under various embedding scenarios, revealing that the changes to the original dataset are minimal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Normal distribution

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Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model 27th

Automated Maternal Fetal Ultrasound Image Identification Usi...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Krishna, Thunakala Bala Poreddy, Ajay Kumar Reddy Sindhu, Kolla Gnapika Kokil, Priyanka Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031781940

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. The accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. This paper proposes a novel approach using a Hybrid Vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. The proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. The effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. The experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Gaze2Atten: Analyzing Explainable Gaze Dynamics to Monitor Human Attention 17th

Gaze2Atten: Analyzing Explainable Gaze Dynamics to Monitor H...

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17th International Conference on Intelligent Robotics and Applications

作者： Mu, Fengjun Zhang, Jingting Zou, Chaobin Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

Attention monitoring is an essential task to evaluate the human cognitive status in human-computer interaction. Prior works either employ an inconvenient invasive method or struggle to provide an explainable mechanism between the original human signal and the attention monitoring results. In this paper, we present Gaze2Atten, a dynamics-based cognitive learning approach for monitoring human attention with the non-invasive gaze signal. Gaze2Atten is constructed based on the dynamic system theory, which makes our mechanism explainable in attention modeling and monitoring. The attention-related gaze dynamics model is first learned based on the cognitive dynamics characteristics of humans. Furthermore, we realize an efficient dynamic pattern-matching method to early detect abnormal attention in the humancomputer interaction process. To validate our approach, we designed a serious game to carry out a human-computer interaction behavior, and a parallel gaze data collection of subjects, the analysis shows that Gaze2Atten enables efficient and real-time human attention monitoring.

关键词： Attention Monitoring Deterministic Learning Human-computer Interface

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6-DOF Motion Blur Synthesis and Performance Evaluation of Object Detection 27th

6-DOF Motion Blur Synthesis and Performance Evaluation of Ob...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Hanjin Li, Feng Zhang, Lei School of Computer Science and Technology Donghua University Shanghai201600 China National Innovation Center of Advanced Dyeing and Finishing Technology Shandong Tai’an271000 China

ISBN: (纸本)9783031784439

The current state-of-the-art deep learning vision networks commonly employs synthetic approaches for data augmentation when confronted with scenes requiring motion blur. However, existing blur synthesis methods often fall short in accurately simulating motion blur as observed in real-world scenarios, consequently hindering the generalization capability of trained deep visual networks to real-world applications, a phenomenon known as domain shift effects [1]. To address this problem, we propose a novel non-uniform motion blur synthesis method for data augmentation. First, we randomly generate the camera’s motion trajectory using a more general six-degree-of-freedom (6-DOF) camera motion model, and then map this trajectory to pixel-level blur kernels. To efficiently perform spatially varying convolution, we employ non-negative matrix factorization (NMF) to decompose those blur kernels into a set of kernel basis and their corresponding mixing coefficients. This enables parallel execution of spatial variation convolutions, thereby significantly improving the efficiency of blur synthesis. Our experiments demonstrate consistently superior results of the proposed method on publicly available real datasets RealBlur, as well as synthetic datasets GoPro and REDS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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