研究者詳細 - 張　海波

ジヤン　ハイボ

張　海波

ZHANG Haibo

Scopus 論文情報

総論文数: 0 総Citation: 0 h-index: 4

Citation Countは当該年に発表した論文の被引用数

所属

大学院情報工学研究院知能情報工学研究系

職名

助教

外部リンク

研究キーワード

機械学習頑健性
コンピュータビジョン
画像識別

取得学位

九州大学 - 博士（情報科学） 2024年03月

学内職務経歴

2024年04月 - 現在九州工業大学大学院情報工学研究院知能情報工学研究系助教

所属学会・委員会

2025年04月 - 現在情報処理学会 CSEC研究会日本国
2024年04月 - 現在 IEEE女性技術者協会アメリカ合衆国
2024年04月 - 現在アメリカ電気電子学会アメリカ合衆国
2024年04月 - 現在情報処理学会日本国

論文

LGNMNet-RF: Micro-Expression Detection Using Motion History Images 査読有り国際誌

Teng M.K.K., Zhang H., Saitoh T.

Algorithms 17 ( 11 ) 2024年11月

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記述言語：英語掲載種別：研究論文（学術雑誌）

Micro-expressions are very brief, involuntary facial expressions that reveal hidden emotions, lasting less than a second, while macro-expressions are more prolonged facial expressions that align with a person’s conscious emotions, typically lasting several seconds. Micro-expressions are difficult to detect in lengthy videos because they have tiny amplitudes, short durations, and frequently coexist alongside macro-expressions. Nevertheless, micro- and macro-expression analysis has sparked interest in researchers. Existing methods use optical flow features to capture the temporal differences. However, these optical flow features are limited to two successive images only. To address this limitation, this paper proposes LGNMNet-RF, which integrates a Lite General Network with MagFace CNN and a Random Forest classifier to predict micro-expression intervals. Our approach leverages Motion History Images (MHI) to capture temporal patterns across multiple frames, offering a more comprehensive representation of facial dynamics than optical flow-based methods, which are restricted to two successive frames. The novelty of our approach lies in the combination of MHI with MagFace CNN, which improves the discriminative power of facial micro-expression detection, and the use of a Random Forest classifier to enhance interval prediction accuracy. The evaluation results show that this method outperforms baseline techniques, achieving micro-expression F1-scores of 0.3019 on CAS(ME)<sup>2</sup> and 0.3604 on SAMM-LV. The results of our experiment indicate that MHI offers a viable alternative to optical flow-based methods for micro-expression detection.

DOI： 10.3390/a17110491

Scopus

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Experimental Exploration of the Power of Conditional GAN in Image Reconstruction-Based Adversarial Attack Defense Strategies 査読有り

Haibo Zhang, Kouichi Sakurai

Advanced Information Networking and Applications, Lecture Notes on Data Engineering and Communications Technologies ( Springer, Cham. ) 201 151 - 162 2024年04月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Adversarial attacks pose a significant threat to the reliability and security of deep learning models, particularly in image processing applications. Defending against these sophisticated manipulations requires innovative strategies, with Generative Adversarial Networks (GANs) emerging as a promising solution. This paper presents an experimental exploration of the power of conditional Generative Adversarial Networks (cGANs) in image reconstruction-based strategies for defending against adversarial attacks. Our study involves a comparative analysis of four distinct image reconstruction models: the traditional GAN-based Defense-GAN, the cGAN-based method exemplified by pix2pix, a hybrid approach combining pix2pix with perceptual loss, and a generator model centered around residual blocks. The results of our experiments demonstrate that cGAN models exhibit significantly enhanced efficacy in defending against adversarial attacks compared to other image reconstruction methods. This superiority is attributed to the inherent characteristics of cGANs, which we delve into in detail. The findings provide crucial insights for developing more robust defense strategies against adversarial attacks in diverse image processing and machine learning applications.

DOI： 10.1007/978-3-031-57870-0_14

DOI： 10.1007/978-3-031-57870-0_14

Kyutacar
One Pixel Adversarial Attack for Fooling Deep Learning-Based Facial Expression Recognition Systems 査読有り国際誌

Kumar P., Seal A., Mohanty S.K., Zhang H., Sakurai K.

Proceedings 2024 IEEE Conference on Dependable and Secure Computing DSC 2024 23 - 30 2024年01月

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記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

It is imperative to think about the security requirements of humanizing artificial intelligence because facial expression recognition (FER) is necessary for human-computer interaction. According to past studies, adding relatively modest perturbations to the input vector makes it simple to change the output of deep learning (DL)-based models. However, research on adversarial examples that target FER systems is still in its infancy. Thus, we analyze a black-box attack under a very constrained condition, where only one pixel can be changed in this study. To do this, we suggest a novel technique based on particle swarm optimization (PSO) that generates adversarial perturbations at the level of a single pixel in a superpixel of a face image to fool two popular DL-based FER systems, such as FER-net, ResNet50, and VGG16. All the experiments are performed on three publicly available benchmark datasets, such as Japanese Female Facial Expression (JAFFE), Extended Cohn-Kanade (CK+), and FED-RO, in two various attack scenarios, such as untargeted and targeted. On the one hand, the success rates of the proposed method on JAFFE, CK+, and FED-RO are 62.5%, 26.12%, and 42.5%, respectively, while fooling FER-net in untargeted attack scenarios. On the other hand, the success rates of the proposed method on JAFFE, CK+, and FED-RO are 77.14%, 50%, and 48.71%, respectively, while fooling VGG16 in an untargeted attack scenario. The findings demonstrate that the proposed method outranks one well-known differential evolution-based pioneering approach.

DOI： 10.1109/DSC63325.2024.00016

Scopus

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A Review on Machine Unlearning 査読有り国際誌

Zhang H., Nakamura T., Isohara T., Sakurai K.

SN Computer Science 4 ( 4 ) 2023年07月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Recently, an increasing number of laws have governed the useability of users’ privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropriate erasure. Therefore, researchers propose various privacy-preserving methods to deal with such issues as machine unlearning. This paper provides an in-depth review of the security and privacy concerns in machine learning models. First, we present how machine learning can use users’ private data in daily life and the role that the GDPR plays in this problem. Then, we introduce the concept of machine unlearning by describing the security threats in machine learning models and how to protect users’ privacy from being violated using machine learning platforms. As the core content of the paper, we introduce and analyze current machine unlearning approaches and several representative results and discuss them in the context of the data lineage. Furthermore, we also discuss the future research challenges in this field.

DOI： 10.1007/s42979-023-01767-4

Kyutacar

Scopus

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Eliminating Adversarial Perturbations Using Image-to-Image Translation Method 査読有り国際誌

Zhang H., Yao Z., Sakurai K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13907 LNCS 601 - 620 2023年01月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Convolutional neural networks are widely used for image recognition tasks, but they are vulnerable to adversarial attacks that can cause the model to misclassify an image. Such attacks pose a significant security risk in safety-critical applications like facial recognition and autonomous driving. Researchers have made progress in defending against adversarial attacks through two approaches: enhancing the neural networks themselves to be more robust and removing the perturbation added to the image through pre-processing. This paper is based upon a recent defense model that belongs to the latter approach, which utilizes image-to-image translation to regenerate images perturbed by adversarial attacks. We optimized the training process of their model and tested the model performance against more recent and strong attacks. The results show that the model is able to regenerate images attacked by the state-of-the-art attack, the AutoAttack, and restores the classification accuracy to a level over 83% to that of the original images.

DOI： 10.1007/978-3-031-41181-6_32

Kyutacar

Scopus

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Conditional Generative Adversarial Network-Based Image Denoising for Defending Against Adversarial Attack 査読有り国際誌

Zhang H., Sakurai K.

IEEE Access 9 169031 - 169043 2021年01月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Deep learning has become one of the most popular research topics today. Researchers have developed cutting-edge learning algorithms and frameworks around deep learning, applying them to a wide range of fields to solve real-world problems. However, we are more concerned about the security risks associated with deep learning models, such as adversarial attacks, which this article will discuss. Attackers can use the deep learning model to create the conditions for an attack, maliciously manipulating the input images to deceive the classification model and produce false positives. This paper proposes a method of pre-denoising all input images to prevent adversarial attacks by adding a purification layer before the classification model. The method in this paper is proposed based on the basic architecture of Conditional Generative Adversarial Networks. It adds the image perception loss to the original algorithm Pix2pix to achieve more efficient image recovery. Our method can recover noise-attacked images to a level close to the actual image to ensure the correctness of the classification results. Experimental results show that our approach can quickly recover noisy images, and the recovery accuracy is 20.22% higher than the previous state-of-the-art.

DOI： 10.1109/ACCESS.2021.3137637

Kyutacar

Scopus

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A Survey of Software Clone Detection from Security Perspective 査読有り国際誌

Zhang H., Sakurai K.

IEEE Access 9 48157 - 48173 2021年01月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

For software engineering, if two code fragments are closely similar with minor modifications or even identical due to a copy-paste behavior, that is called software/code clone. Code clones can cause trouble in software maintenance and debugging process because identifying all copied compromised code fragments in other locations is time-consuming. Researchers have been working on code clone detection issues for a long time, and the discussion mainly focuses on software engineering management and system maintenance. Another considerable issue is that code cloning provides an easy way to attackers for malicious code injection. A thorough survey work of code clone identification/detection from the security perspective is indispensable for providing a comprehensive review of existing related works and proposing future potential research directions. This paper can satisfy above requirements. We review and introduce existing security-related works following three different classifications and various comparison criteria. We then discuss three further research directions, (i) deep learning-based code clone vulnerability detection, (ii) vulnerable code clone detection for 5G-Internet of Things devices, and (iii) real-time detection methods for more efficiently detecting clone attacks. These methods are more advanced and adaptive to technological development than current technologies, and still have enough research space for future studies.

DOI： 10.1109/ACCESS.2021.3065872

Kyutacar

Scopus

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Gaze Attention Estimation for Medical Environments 査読有り国際誌

Natchapol Shinno, Yuki Furuya, Takeshi Saitoh, Haibo Zhang, Keiko Tsuchiya, Hitoshi Sato, Frank Coffey

Proceedings of MVA 2025 - 19th International Conference on Machine Vision and Applications 2025年07月

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記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Japan Kyoto 2025年07月26日 - 2025年07月28日
Detecting Advanced Persistent Threat Exfiltration with Ensemble Deep Learning Tree Models and Novel Detection Metrics 査読有り国際誌

Cai X., Zhang H., Ahmed C.M., Koide H.

IEEE Access 13 81803 - 81822 2025年01月

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記述言語：英語掲載種別：研究論文（学術雑誌）

Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to detect and halt APT data exfiltration, these attacks continue to pose significant threats to sensitive information and result in substantial commercial losses. Current approaches primarily focus on preemptive measures, which are insufficient once early-stage detection fails due to a lack of continuous monitoring. We propose an effective and efficient network monitoring method to address this gap and detect APT exfiltration during data transfer. Our approach assumes the presence of an undetected APT attacker within the victim system. We examine data exfiltration across three exfiltration traffic environments: exfiltration over command control channels, exfiltration over transfer size limitations, and their combinations. We introduce two detection metrics: Package Transfer Rate and Byte Transfer Rate. Utilizing these metrics, we measure network traffic, categorize APT attack environments, and train deep neural network models, named EDXGB, using ensembled decision trees to predict APT exfiltration. Our method is validated on two public datasets and compared against six baseline methods. Additionally, we simulate real-world exfiltration scenarios by creating three exfiltration traffic environments for each dataset. The results demonstrate that our method effectively detects APT exfiltration across various network environments, enhancing data protection and secure transfer. The code is open source and available at https://github.com/cxjuan/EDXGB-for-APT.

DOI： 10.1109/ACCESS.2025.3567772

Scopus

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Detection of Script Reading Face in Diet Deliberation Video 査読有り

Yamaguchi T., Saitoh T., Zhang H.

Lecture Notes in Electrical Engineering 1322 LNEE 189 - 198 2025年01月

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記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1007/978-981-96-1535-3_20

DOI： 10.1007/978-981-96-1535-3_20
Gaze Attention Estimation for Medical Environments 国際誌

Shinno Natchapol, Haibo Zhang, Takeshi Saitoh

12th International Symposium on Applied Engineering and Sciences (SAES2024) 2024年11月

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記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Japan Kitakyushu 2024年11月14日 - 2024年11月15日
Leveraging Image-to-Image Translation for Enhanced Adversarial Defense 国際誌

Haibo Zhang, Kouichi Sakurai, Takeshi Saitoh

12th International Symposium on Applied Engineering and Sciences (SAES2024) 2024年11月

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担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Japan Kitakyushu 2024年11月14日 - 2024年11月15日
機械アンラーニングの研究に関する現状と課題

張海波，櫻井幸一

人工知能 ( 一般社団法人人工知能学会 ) 38 ( 2 ) 197 - 205 2023年03月

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担当区分：筆頭著者記述言語：日本語掲載種別：記事・総説・解説・論説等（学術雑誌）

DOI： 10.11517/jjsai.38.2_197

DOI： 10.11517/jjsai.38.2_197

Kyutacar
POSTER: A Fine-Grained Metric for Evaluating the Performance of Adversarial Attacks and Defenses 査読有り国際誌

Zhang H., Yao Z., Sakurai K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13907 LNCS 690 - 694 2023年01月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Over the past decade, the development of both adversarial attack methods and defense strategies has accelerated rapidly. Classification accuracy has been predominantly used as the sole metric for assessing model performance. However, when the reported accuracy rates of two models are identical or very similar, it becomes challenging to determine which model is superior. To address this issue and offer more insights into model performance, this study introduces a novel classification performance metric: the confidence gap. This metric is defined as the difference in confidence level between the true label and either the top 1 prediction or the second-best prediction, depending on the accuracy of the image classification. The confidence level, as indicated by its sign, reflects the correctness of the classification and provides more detailed information on the robustness of the classification result. Recognizing that evaluation results may be inconsistent when employing different criteria, we recommend that future research in this field should report the confidence gap alongside accuracy rates.

DOI： 10.1007/978-3-031-41181-6_41

Kyutacar

Scopus

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A Design of Network Attack Detection Using Causal and Non-causal Temporal Convolutional Network 査読有り国際誌

He P., Zhang H., Feng Y., Sakurai K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14299 LNCS 513 - 523 2023年01月

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記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Temporal Convolution Network(TCN) has recently been introduced in the cybersecurity field, where two types of TCNs that consider causal relationships are used: causal TCN and non-causal TCN. Previous researchers have utilized causal and non-causal TCNs separately. Causal TCN can predict real-time outcomes, but it ignores traffic data from the time when the detection is activated. Non-causal TCNs can forecast results more globally, but they are less real-time. Employing either causal TCN or non-causal TCN individually has its drawbacks, and overcoming these shortcomings has become an important topic. In this research, we propose a method that combines causal and non-causal TCN in a contingent form to improve detection accuracy, maintain real-time performance, and prevent long detection time. Additionally, we use two datasets to evaluate the performance of the proposed method: NSL-KDD, a well-known dataset for evaluating network intrusion detection systems, and MQTT-IoT-2020, which simulates the MQTT protocol, a standard protocol for IoT machine-to-machine communication. The proposed method in this research increased the detection time by about 0.1ms compared to non-causal TCN when using NSL-KDD, but the accuracy improved by about 1.5%, and the recall improved by about 4%. For MQTT-IoT-2020, the accuracy improved by about 3%, and the recall improved by about 7% compared to causal TCN, but the accuracy decreased by about 1% compared to non-causal TCN. The required time was shortened by 30ms (around 30%), and the recall was improved by about 7%.

DOI： 10.1007/978-3-031-45933-7_30

Kyutacar

Scopus

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Blockchain for IoT-Based Digital Supply Chain: A Survey 査読有り

Haibo Zhang, Kouichi Sakurai

Advances in Internet, Data and Web Technologies, Lecture Notes on Data Engineering and Communications Technologies. ( Springer, Cham ) 47 564 - 573 2020年01月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Japan Kitakyushu 2020年02月24日 - 2020年02月26日

This exploratory investigation aims to discuss current network environment of digital supply chain system and security issues, especially from the Internet world, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things and blockchain, for improving various system performance and properties. This paper introduces the general histories and backgrounds, in terms of information science, of the supply chain and relevant technologies which have been applied or are potential to be applied on supply chain with purpose of lowering cost, facilitating its security and convenience. It provides a comprehensive review of current relative research work and industrial cases from several famous companies. It also illustrates the IoT enablement and security issues of current digital supply chain system, and existing blockchain’s role in this kind of digital system. Finally, this paper concludes several potential or existing security issues and challenges which supply chain management is facing.

DOI： 10.1007/978-3-030-39746-3_57

DOI： 10.1007/978-3-030-39746-3_57

Kyutacar
Security and trust issues on digital supply chain 査読有り国際誌

Zhang H., Nakamura T., Sakurai K.

Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 338 - 343 2019年08月

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担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This exploratory investigation aims to discuss current status and challenges, especially in aspect of security and trust problems, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things, cloud computing and blockchain, for improving various system performance and properties, i.e. transparency, visibility, accountability, traceability and reliability. This paper introduces the general histories and definitions, in terms of information science, of the supply chain and relevant technologies which have been applied or are potential to be applied on supply chain with purpose of lowering cost, facilitating its security and convenience. It provides a comprehensive review of current relative research work and industrial cases from several famous companies. It also illustrates requirements or performance of digital supply chain system, security management and trust issues. Finally, this paper concludes several potential or existing security issues and challenges which supply chain management is facing.

DOI： 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00069

Kyutacar

Scopus

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▼全件表示

著書

Blockchain for IoT-Based Digital Supply Chain: A Survey 査読有り

Zhang H., Sakurai K.（共著）

Lecture Notes on Data Engineering and Communications Technologies 2020年01月

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記述言語：英語

This exploratory investigation aims to discuss current network environment of digital supply chain system and security issues, especially from the Internet world, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things and blockchain, for improving various system performance and properties. This paper introduces the general histories and backgrounds, in terms of information science, of the supply chain and relevant technologies which have been applied or are potential to be applied on supply chain with purpose of lowering cost, facilitating its security and convenience. It provides a comprehensive review of current relative research work and industrial cases from several famous companies. It also illustrates the IoT enablement and security issues of current digital supply chain system, and existing blockchain’s role in this kind of digital system. Finally, this paper concludes several potential or existing security issues and challenges which supply chain management is facing.

DOI： 10.1007/978-3-030-39746-3_57

Scopus

口頭発表・ポスター発表等

日本語文章読唇における生成発話シーンの利用とその効果の検証

佐藤光希, 張海波, 齊藤剛史

第28回　画像の認識・理解シンポジウム（MIRU2025）

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開催期間： 2025年07月29日 - 2025年08月01日記述言語：日本語開催地：国立京都国際会館国名：日本国
中間表現文字列と自然言語処理を用いた日本語文章読唇

甲斐啓悟, 張海波, 齊藤剛史

第28回　画像の認識・理解シンポジウム（MIRU2025）

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開催期間： 2025年07月29日 - 2025年08月01日記述言語：日本語開催地：国立京都国際会館国名：日本国
口形認識を利用した文字入力システム

竹内大輔, 齊藤剛史, 伊藤和幸, 張海波

福祉情報工学研究会電子情報通信学会

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開催期間： 2025年03月10日記述言語：日本語開催地：筑波技術大学
360度カメラを利用した頭部姿勢推定と深度推定に基づく共同注視検出

赤嶺諒, 古谷優樹, 張海波, 齊藤剛史, 土屋慶子

動的画像処理実利用化ワークショップ2025（DIA2025）精密工学会画像応用技術専門委員会

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開催期間： 2025年03月05日 - 2025年03月06日記述言語：日本語開催地：きらめきみなと館国名：日本国
ドイツ語の文章読唇用データセットの構築

林優毅, 齊藤剛史, 張海波

第11回サイレント音声認識ワークショップ（SSRW2025）

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開催期間： 2025年03月01日記述言語：日本語開催地：那覇商工会議所＆オンライン（ハイブリッド開催）
日本語文章発話シーンに対する母音レベル読唇に関する研究

甲斐啓悟, 齊藤剛史, 張海波

第11回サイレント音声認識ワークショップ（SSRW2025）

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開催期間： 2025年03月01日記述言語：日本語開催地：那覇商工会議所＆オンライン（ハイブリッド開催）
発話シーンから音声データの生成に関する研究

脇坂伸, 齊藤剛史, 張海波

第11回サイレント音声認識ワークショップ（SSRW2025）

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開催期間： 2025年03月01日記述言語：日本語開催地：那覇商工会議所＆オンライン（ハイブリッド開催）
神経難病患者の発話シーンに対するCNNとSpatial-GCNを統合した口形認識<

権藤優季, 齊藤剛史, 伊藤和幸, 張海波

バイオメトリクスと認識・認証シンポジウム（SBRA2024）電子情報通信学会バイオメトリクス研究専門委員会

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開催期間： 2024年12月10日 - 2024年12月11日記述言語：日本語開催地：伊香保温泉ホテル天坊
生成モデルによる文章読唇精度向上の検証

佐藤光希, 齊藤剛史, 張海波

バイオメトリクスと認識・認証シンポジウム（SBRA2024）電子情報通信学会バイオメトリクス研究専門委員会

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開催期間： 2024年12月10日 - 2024年12月11日記述言語：日本語開催地：伊香保温泉ホテル天坊
Multi-Input Late Fusion in LGNMNet-RF for Micro-Expression Detection

Matthew Kit Khinn Teng, Haibo Zhang, Takeshi Saitoh

バイオメトリクスと認識・認証シンポジウム（SBRA2024）電子情報通信学会バイオメトリクス研究専門委員会

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開催期間： 2024年12月10日 - 2024年12月11日記述言語：英語開催地：伊香保温泉ホテル天坊
眼球運動を利用したALS患者用の眼鏡型スイッチの開発（第2報）

玉井龍斗, 齊藤剛史, 伊藤和幸, 張海波

福祉情報工学研究会電子情報通信学会

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開催期間： 2024年12月04日 - 2024年12月05日記述言語：日本語開催地：産業技術総合研究所臨海副都心センター

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学術関係受賞

Best Paper Award

ICGEC 2024 Detection of Script Reading Face in Diet Deliberation Video 2024年08月28日

Tsuyoshi Yamaguchi, Takeshi Saitoh, Haibo Zhang

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受賞国：日本国

海外研究歴

拡散モデルを⽤いた機械学習システムの防御機構強する研究

ブリティッシュコロンビア⼤学カナダ研究期間: 2025年04月01日 - 2026年01月31日

学会・委員会等活動

情報処理学会 CSEC研究会運営委員

2025年04月 - 現在
The Pacific Rim International Conference on Artificial Intelligence program committee member

2023年11月 - 現在