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7th International Conference on Artificial intelligence and Security, ICAIS 2021

作者： Yu, Mujian Luo, Junwei Xu, Bozhi Chen, Guoliang Lu, Wei School of Computer Science and Engineering Guangdong Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangdong Science and Technology Innovation Monitoring and Research Center Guangzhou510033 China

ISBN: (纸本)9783030786113

Many advanced halftone steganographic schemes focus only on the distortion of human visual perception or the distortion according to statistics. In this paper, a halftone image steganography based on minimizing distortion with density transition is proposed which aims at utilizing the entropy model and pixel density to resist potential steganalysis. First, the entropy model is established on the image database to describe the texture content and transformed to the preliminary distortion score map. Because the form of texture presentation is distinct between halftone images and ordinary binary images, the feature of pixel density is introduced to represent the local intensity in images. Then the pixel density transition adjustment based on the entropy model is presented, which makes the distortion score more reliable. The final additive distortion map is generated by combining the entropy model and the strategy of density transition. To play the advantage of distortion measurement, syndrome-trellis code (STC) is applied with the distortion map to minimize the embedding distortions. Experimental results demonstrate that compared with other halftone steganographic schemes, the proposed method achieves high statistical security and great visual quality with considerable embedding capacity. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Answer-Centric Local and Global Information Fusion for Conversational Question Generation 12

Answer-Centric Local and Global Information Fusion for Conve...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Lei, Panpan Sun, Xiao Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9781665438582

Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results. © 2021 IEEE.

关键词： Neural network models

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EmoDamp: A Model for Speaker Emotion Inference in Dialogues with Bidirectional Emotion Damping Mechanism 6

EmoDamp: A Model for Speaker Emotion Inference in Dialogues ...

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6th International Conference on Algorithms, computing and Artificial intelligence, ACAI 2023

作者： Jiang, Yueqi Sun, Xiao Wang, Jiamin AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400709203

Emotion analysis in conversation has been a popular research topic in the natural language processing field. While much of the existing research has focused on emotion recognition in conversation, the emotion inference task in conversation is more challenging due to its specific prerequisites. In this paper, we propose EmoDamp, a novel model for the conversational emotion inference task. By referring to psychological theories, a bidirectional emotion damping network(BED Net) is designed to model the emotion generation mechanism. Furthermore, external commonsense knowledge is also used to supplement the prediction results to make them more consistent with authentic logic. Finally, a conditional random field (CRF) is used to capture the potential constraints of the emotion transfer process in conversation and simulate emotion contagion. Experimental results on two benchmark datasets demonstrate that EmoDamp outperforms the baseline model, achieving improved results on each emotion category and showcasing its effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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Halftone Image Steganalysis by Reconstructing Grayscale Image 7th

Halftone Image Steganalysis by Reconstructing Grayscale Imag...

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7th International Conference on Artificial intelligence and Security, ICAIS 2021

作者： Luo, Junwei Lin, Cong Zeng, Lingwen Liang, Jifan Lu, Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China

ISBN: (纸本)9783030786113

Utilizing some special pixels patterns constructing the steganalytic features is popular in binary image steganalysis, which could be also used in halftone image. There is almost no specific image steganalysis for halftone image. In this paper, a halftone image steganalysis scheme is proposed and achieves a satisfactory performance, which is totally different from the previous works focussing on some special pixels patterns. Inspired by the fact that halftoning techniques based on the low-pass characteristic of human visual system model (HVS model), the grayscale image is considered reconstructing with a Gaussian filter. And the distortions caused by embedding secret messages will still exist. After that, some common grayscale image steganalysis can be used for extracting the steganalytic features. Furthermore, a series of experiments are conducted and the experimental results show that the proposed scheme is effective on halftone image steganalysis. © 2021, Springer Nature Switzerland AG.

关键词： Binary images

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BAT: A Versatile Bipartite Attention-based Approach for Comprehensive Truth Inference in Mobile Crowdsourcing

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jiacheng Tang, Feilong Liu, Hao Chen, Long Yu, Yichuan Zhu, Yanmin Yu, Jiadi Hou, Xiaofeng Heng, Pheng-Ann The Chinese University of Hong Kong Department of Computer Science and Engineering 999077 Hong Kong Shanghai Jiao Tong University School of Computer Science Shanghai200240 China East China Normal University School of Data Science and Engineering China Simon Fraser University School of Computing Science Canada Huawei Technologies Co. Ltd China Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios. © 2002-2012 IEEE.

关键词： Graph neural networks

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Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models 38

Frozen-DETR: Enhancing DETR with Image Understanding from Fr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518055 China Tongyi Lab Alibaba Group China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China Guangdong Guangzhou510555 China

Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely overlooked, especially without fine-tuning them. In this work, we show that frozen foundation models can be a versatile feature enhancer, even though they are not pre-trained for object detection. Specifically, we explore directly transferring the high-level image understanding of foundation models to detectors in the following two ways. First, the class token in foundation models provides an in-depth understanding of the complex scene, which facilitates decoding object queries in the detector's decoder by providing a compact context. Additionally, the patch tokens in foundation models can enrich the features in the detector's encoder by providing semantic details. Utilizing frozen foundation models as plug-and-play modules rather than the commonly used backbone can significantly enhance the detector's performance while preventing the problems caused by the architecture discrepancy between the detector's backbone and the foundation model. With such a novel paradigm, we boost the SOTA query-based detector DINO from 49.0% AP to 51.9% AP (+2.9% AP) and further to 53.8% AP (+4.8% AP) by integrating one or two foundation models respectively, on the COCO validation set after training for 12 epochs with R50 as the detector's backbone. © 2024 Neural information processing systems foundation. All rights reserved.

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New Entanglement-Assisted Concatenated Quantum Codes Exceed Quantum Gilbert-Varshamov Bound

New Entanglement-Assisted Concatenated Quantum Codes Exceed ...

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International Conference on Computer and Communications (ICCC)

作者： Renxu Wang Jihao Fan School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Quantum error-correction codes leverage encoding and redundancy to effectively detect and correct errors, thereby enhancing the reliability and accuracy of signal detection. Based on the scheme of entanglement-assisted concatenated quantum codes (EACQCs), we construct both new binary and non-binary EACQCs. Several of the new EACQCs achieve better parameters than the best-known standard entanglement-assisted quantum error-correction codes while maintaining the same code lengths and net transmissions. The quantum Gilbert-Varshamov (GV) bound serves as a crucial benchmark for evaluating quantum code parameters. As the code length increases, we construct binary and non-binary EACQCs that exceed the quantum GV bound. Index Terms-entanglement-assisted quantum error-correction code, entanglement-assisted concatenated quantum codes, quan-tum Gilbert-Varshamov bound

关键词： Quantum computing Accuracy Quantum entanglement Redundancy Benchmark testing Encoding Error correction codes Reliability Standards Signal detection

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Genetic and phenotypic relationships between coronary atherosclerotic heart disease and electrocardiographic traits

Genetic and phenotypic relationships between coronary athero...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xinfeng Zhang, Haoyang Xiu, Xuehao Qi, Mengling Yang, Yuedong Zhao, Huiying Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe School of Computer Science and Engineering Guangzhou China Sun Yat-sen Memorial Hospital Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical Research Center Guangzhou China

ISBN: (纸本)9781665468190

Observational studies have revealed that Coronary Atherosclerotic Heart Disease (CAHD) is associated with abnormal electrocardiogram (ECG) traits. However, it remains unclear whether there are genetic correlations between ECG and CAHD. Here, we explored genetic correlations and putative causal relationships between CAHD and ECG by performing Mendelian randomization (MR) and Polygenic risk score (PRS) analyses on the summary statistics from a large-scale genome-wide association study (GWAS) for CAHD (FinnGen: Ncase 23363, Ncontrol 187840) and ECG traits (UK Biobank: Ncase=1137, Ncontrol=40823). Results showed a causal genetic relationship between CAHD and six ECG traits in the lead V6. These ECG traits combining with age and gender have predicted CAHD risk with an AUC of 0.76. Further summary data-based Mendelian randomization (SMR) analysis identified 11 risk genes associated with the causality between CAHD and ECG. Thus, the revealed putative causal effects of CAHD on ECG traits provide genetic evidence to support the importance of monitoring CAHD risk through the ECG. © 2022 IEEE.

关键词： Electrocardiograms

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LLMUZZ: LLM-based seed optimization for black-box device fuzzing

LLMUZZ: LLM-based seed optimization for black-box device fuz...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Guangming Gao Shuitao Gan Xiaofeng Wang Shengkai Zhu School of Artificial Intelligence and Computer Science Jiangnan University China Labortory for Advanced Computing and Intelligence Engineering Wuxi China Pengcheng Laboratory Shenzhen China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

As an increasing number of Internet of Things (IoT) devices are being deployed, the threat from vulnerabilities inside these devices is growing. Fuzzing is a primary method used for discovering vulnerabilities in IoT devices. The quality of the initial seeds and the seed mutation strategy are two crucial components of fuzzing that largely determine the effectiveness of the fuzzing process. However, owing to the diversity of IoT devices and the highly structured nature of inputs, designing universal seed generation and mutation strategies is extremely challenging. In this paper, we propose LLMUZZ, which is a large language model (LLM)-based black-box fuzzing approach for IoT devices. Specifically, we employ prompt engineering techniques in few-shot learning, using HTML form data from frontend files and an example HTTP request as inputs to LLMs to generate initial seeds. Then, we input the requests to be mutated into LLMs to identify the fields requiring mutation, thereby assisting in the seed mutation process. This approach ensures that the mutated seeds remain valid. Additionally, static analysis methods are utilized to discover hidden keywords within the firmware, thereby further expanding the initial seeds. In the experiments, we implement a prototype of LLMUZZ and evaluate it on 8 different IoT devices. A total of 16 previously unknown vulnerabilities are found, for which we have received 4 CVEs; the remaining vulnerabilities still under review, demonstrating that LLMUZZ has a strong capacity for vulnerability discovery.

关键词： Reviews Large language models Closed box Prototypes Static analysis Fuzzing Internet of Things Security Prompt engineering Microprogramming

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Labrador: Response Guided Directed Fuzzing for Black-box IoT Devices

Labrador: Response Guided Directed Fuzzing for Black-box IoT...

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IEEE Symposium on Security and Privacy

作者： Hangtian Liu Shuitao Gan Chao Zhang Zicong Gao Hongqi Zhang Xiangzhi Wang Guangming Gao Information Engineering University Tsinghua University Laboratory for Advanced Computing and Intelligence Engineering Zhongguancun Laboratory Henan Key Laboratory of Information Security University of Electronic Science and Technology of China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Fuzzing is a popular solution to finding vulnerabilities in software including IoT firmware. However, due to the challenges of emulating or rehosting firmware, some IoT devices (e.g., enterprise-level devices) can only be fuzzed in a black-box manner, which makes fuzzers blind and inefficient due to missing feedbacks (e.g., code coverage or distance). In this paper, we present a novel response guided directed fuzzing solution Labrador, able to test black-box IoT devices efficiently. Specifically, we leverage the network response to infer the execution trace of firmware and deduce the code coverage of testing. Second, we leverage the test case (i.e., request) and its response to estimate the distance to the target sensitive code (i.e., sink). Lastly, we further leverage the distance to guide test case mutation, which efficiently drives directed fuzzing toward candidate vulnerable code. We have implemented a prototype of Labrador and evaluated it on 14 different enterprise-level IoT devices. Results showed that Labrador significantly outperforms state-of-the-art (SOTA) solutions. It finds 44X more vulnerabilities than SNIPUZZ, BOOFUZZ and FIRM-AFL and 8.57X more vulnerabilities than SaTC. In total, it discovered 79 unknown vulnerabilities, of which 61 were assigned with CVEs.

关键词： Privacy Codes Closed box Prototypes Fuzzing Drives Internet of Things

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