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International Conference on Software engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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ModelLock: Locking Your Model With a Spell

arXiv

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arXiv 2024年

作者： Gao, Yifeng Sun, Yuhua Ma, Xingjun Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

This paper presents a novel model protection paradigm Model Locking that locks the performance of a finetuned model on private data to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the private finetuning data into unique styles or blend new objects into the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the same text prompt used to edit the data. We conduct extensive experiments on both image classification and segmentation tasks and show that 1) ModelLock can effectively lock finetuned models without significantly reducing their unlocked performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models. Copyright © 2024, The Authors. All rights reserved.

关键词： Keys (for locks)

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

Point-Level and Set-Level Deep Representation Learning for C...

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International Conference on Computer and Communications (ICCC)

作者： Jihui Hu Pengfei Ye Danyang Li Lingyun Dong Xiaopan Chen Xiaoke Zhu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.

关键词： Representation learning Training Image color analysis Computational modeling Task analysis

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Analysis of blockchain protocol against static adversarial miners corrupted by long delay attackers

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Science China(Information Sciences) 2020年第3期63卷 49-63页

作者： Quan YUAN Puwen WEI Keting JIA Haiyang XUE Key Laboratory of Cryptologic Technology and Information Security Ministry of EducationShandong University Department of Computer Science and Technology Tsinghua University State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences

Bitcoin, which was initially introduced by Nakamoto, is the most disruptive and impactive cryptocurrency. The core Bitcoin technology is the so-called blockchain protocol. In recent years, several studies have focused on rigorous analyses of the security of Nakamoto’s blockchain protocol in an asynchronous network where network delay must be considered. Wei, Yuan, and Zheng investigated the effect of a long delay attack against Nakamoto’s blockchain protocol. However, their proof only holds in the honest miner setting. In this study, we improve Wei, Yuan and Zheng’s result using a stronger model where the adversary can perform long delay attacks and corrupt a certain fraction of the miners. We propose a method to analyze the converge event and demonstrate that the properties of chain growth, common prefix, and chain quality still hold with reasonable parameters in our stronger model.

关键词： blockchain bitcoin random oracle delay consensus protocol

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections

Semi-supervised Segmentation and Quantization Algorithm for ...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yulong Cai Xiaopan Chen Qi Hu Lingyun Dong Caihong Yuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods.

关键词： Heart Training Quantization (signal) Semantic segmentation Manuals Rats Size measurement

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Lightweight Transformer Network and Self-supervised Task for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yunwei Li Danyang Li Lingyun Dong Xiaopan Chen School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.

关键词： Training Knowledge engineering Recurrent neural networks Semantic segmentation Self-supervised learning Object detection Transformers

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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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Towards Instruction-Tuned Verification for Improving Biomedical Information Extraction with Large Language Models

Towards Instruction-Tuned Verification for Improving Biomedi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jin Li Ruifan Yuan Yu Tian Jingsong Li School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Biomedical Engineering & Instrumental Science Zhejiang University Hangzhou China Zhejiang Lab Research Center for Data Hub and Security Hangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical information extraction, including tasks like Named Entity Recognition (NER) and Relationship Extraction (RE), plays a critical role in analyzing unstructured biomedical data, facilitating more accurate downstream applications such as clinical decision support and personalized medicine. Although Large Language Models (LLMs) excel in various domains, they often struggle with issues like hallucinations and domain-specific inaccuracies, limiting their effectiveness in biomedical applications. To tackle these challenges, we propose a novel framework that incorporates an external dual-stage verifier to enhance biomedical information extraction. This verifier is prompted to first identify and complete missing entities and relations, and then filter out incorrect extractions, thereby enhancing both precision and recall in information extraction. Furthermore, we proposed an approach for constructing the instruction-tuning dataset using both positive and negative demonstrations, which improves the verifier’s correction capabilities through Parameter-Efficient Fine-Tuning (PEFT). Our extensive experiments on three public benchmark datasets cover both biomedical NER and RE tasks, comparing four LLMs ranging from 7 to 175 billion parameters to validate the effectiveness and scalability of our approach. The results demonstrate that by integrating an external verifier specifically finetuned for biomedical tasks, we achieve substantial improvements in precision, recall, and F1 scores for NER and RE tasks. Our method consistently outperforms In-Context Learning (ICL) approaches in F1 scores, achieving increases of up to 20% in some cases. Furthermore, ablation studies demonstrate the effectiveness of both stages in the verification process. The proposed framework provides valuable insights for accurate biomedical information extraction using LLMs, highlighting its potential applications in real-world clinical settings.

关键词： Accuracy Limiting Large language models Scalability Precision medicine Named entity recognition Information filters Distance measurement data mining Bioinformatics

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