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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Haotian Pan, Chenhui Liu, Yang Zhao, Guoying Li, Xiaobai Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Face forgery detection is crucial in preserving the security and integrity of facial data amidst the rapid developments in face manipulation techniques and deep generative models. Existing methods for video face forgery detection typically assume that all frames in a forged video are manipulated, while identifying partially forged videos with only a subset of altered frames is still a challenge to be solved. To address this issue, we propose a novel framework, i.e., the UVIF, that utilizes additional annotated images to provide fine-grained supervision for detecting partial forgeries in videos. The UVIF integrates a unified encoder and a multi-task learning paradigm to model both facial videos and images for boosted video face forgery detection. A 2D backbone with temporal fusion modules is employed for the unified encoder. A pseudo labeling process is also designed for facial video frames to bridge the representation of individual video frames and static images. Extensive experiments on benchmark datasets demonstrate the effectiveness of our framework, outperforming state-of-the-art methods in detecting partially forged videos while introducing no additional computational overhead. Our code is available at https://***/haotianll/UVIF. © 2024 The Authors.

关键词： Face recognition

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Research on the Design of blockchain-Based Aviation Physical and Chemical Testing System 29th

Research on the Design of Blockchain-Based Aviation Physical...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zhang, Yijian Zhang, Lu Xu, Jing Du, Jingyi Wei, Zunbo Chen, Xiaofeng Hangzhou Qulian Technology Co. Ltd. Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9783031429866

As a part of aviation flight guarantee, physical and chemical testing in the aviation industry plays a very important role. The traditional physical and chemical testing industry has problems such as long process waiting time and slow generation of test results and reports. Although laboratory management systems such as the LIMS (laboratory Information Management System) have improved these problems to a certain extent, the LIMS system has not fundamentally changed the above problems and lead to new problems. Based on the particularity of the aviation physical and chemical testing system, as well as the requirements for high consensus, high synchronization, and high traceability of data, blockchain technology is very suitable as a means of improvement. Based on blockchain technology, this paper designs a new type of the aviation physical and chemical testing system strives to improve the problems existing in the current aviation physical and chemical testing system and improve production efficiency. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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DSBT: Research on Soulbound Token Mechanism Based on Consortium blockchain and Decentralized Identity 29th

DSBT: Research on Soulbound Token Mechanism Based on Consort...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zhang, Yijian Zhang, Lu Zhang, Qing Zheng, Peiyu Jia, Xiangjuan Chen, Xiaofeng Hangzhou Qulian Technology Co. Ltd. Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9783031429866

Issues such as technology, application and future development based on blockchain have always been hot topics. Future forms such as Web3.0 and the Metaverse are inseparable from digital identities and virtual tokens. This paper focuses on the recently popular Soulbound Token (SBT), introduces the concepts of digital identity, blockchain, and Decentralized Identity (DID) related to it, analyzes the shortcomings and challenges of SBT, and combines the consortium blockchain and DID. A new concept of Soulbound Token (DSBT) based on the decentralized identity of the consortium blockchain is proposed, which aims to make up for the technical deficiencies of SBT, and through the new concept of Displayable Credentials (DCs), DSBT combines DID for identity authentication, The advantages of access, in turn, can realize the concept of "programmable privacy" expected by SBT. Finally, the coping mechanism is proposed for the possible risks of DSBT, the application scenarios of DSBT in the active health intelligent care platform for the elderly are listed, and the future development of blockchain, DID and DSBT is expected. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： blockchain

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Exemplar-Free Incremental Deepfake Detection 27

Exemplar-Free Incremental Deepfake Detection

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xiong, Wuti Zhao, Guoying Li, Xiaobai Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Incremental Deepfake Detection (IDD) aims to continuously update models with new domain data, adapting to evolving forgery techniques. Existing works require extra buffers to store old exemplars for maintaining previously learned knowledge. However, it is infeasible when previous data is unavailable due to storage and privacy issues. This paper focuses on a more challenging but practical exemplar-free IDD problem that requests zero old exemplars when updating the model. To address this problem, we design a domain-adaptive module that uses independent adapters to learn domain-specific knowledge for each domain, avoiding using old exemplars. Besides, we introduce an uncertainty optimization strategy to optimize the adapters more efficiently. With excellent scalability, our method can be easily deployed to various models. To simulate the practical scenarios, we designed two new protocols based on diverse deepfake datasets. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods by a large margin. The code is available at https://***/woody-panda/EF-IDD. © 2024 The Authors.

关键词： Differential privacy

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Metaverse Standardization: System Planning, Current Status and Prospects 29th

Metaverse Standardization: System Planning, Current Status a...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Wei, Zunbo Ge, Qiang Jia, Xiangjuan Zhang, Yijian Han, Mengwei Chen, Xiaofeng Hangzhou Qulian Technology Co. Ltd. Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9783031429866

In recent years, metaverse has become one of the frontier fields that countries around the world pay attention to and has gradually entered the public's field of vision. The emergence of metaverse brings new consumption scenarios and models to industrial digitization, and will bring together more industrial resources such as social networking, entertainment, finance, and education, thereby creating a brand new digital world. However, since the metaverse is an emerging technology and business form, it is not perfect in terms of laws, regulations and regulatory policies. At the same time, there are many industry participants in the metaverse, with various business forms, and its development is still in its early stages. There are uncertainties in the global supervision of the metaverse. Standards need to be used to guide it to promote the healthy development of the metaverse. Therefore, it is particularly important to do a good job in the forward-looking analysis of metaverse standardization. From the perspective of standardization, this paper proposes a metaverse standard system based on the current situation of the metaverse, sorts out the existing standards related to the metaverse, and gives some suggestions on the standardization construction of the metaverse. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Standardization

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APIBeh: Learning Behavior Inclination of APIs for Malware Classification 35

APIBeh: Learning Behavior Inclination of APIs for Malware Cl...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Cui, Lei Zhu, Yiran Yin, Junnan Hao, Zhiyu Wang, Wei Liu, Peng Yang, Ziqi Yun, Xiaochun Zhongguancun Laboratory China Zhejiang University The State Key Laboratory of Blockchain and Data Security China China Guangxi Normal University School of Computer Science and Engineering China

ISBN: (纸本)9798350353884

Malware classification involves categorizing mal-ware samples based on their characteristics. While deep learning techniques applied to malware execution traces, mainly API calls, have shown potential in this field, they still perform poorly. This is primarily because they treat all APIs equally and train classifiers directly on native APIs, which inadequately capture the under-lying family-related semantics. In this paper, we first investigate the behaviors of multiple malware families and observe that different families exhibit divergent behaviors, with each family consistently favoring certain behaviors over time. Motivated by this, we propose APIBeh, a new embedding method designed to enhance malware classification. APIBeh first utilizes Benignity Degree Algorithm to identify and exclude insignificant, likely benign APIs from sequences. Then, it introduces the concept of Behavior Inclination, which quantifies the association between an API and malicious behaviors, facilitating high-level behavior encoding for each API. This Behavior Inclination embedding is then concatenated with raw embedding to represent an API, and fed into a DL model for classifier training. Experimental results show that APIBeh outperforms existing embedding methods in classification performance, e.g., 3.18% boost in weighted f1-score over a recent study using word2vec. In addition, it offers robustness to concept drift and adversarial attacks. © 2024 IEEE.

关键词： Embeddings

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Nearly Optimal Regret for Decentralized Online Convex Optimization 37

Nearly Optimal Regret for Decentralized Online Convex Optimi...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Wan, Yuanyu Wei, Tong Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using_only local computations and communications. Previous studies have established O(n5/4ρ-1/2 √T) and O(n3/2ρ-1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ -1/4 √T) and Õ(nρ-1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting the spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ-14 √T) and Ω(nρ-1/2), respectively. These lower bounds suggest that our algorithms are nearly optimal in terms of T, n, and ρ. © 2024 Y. Wan, T. Wei, M. Song & L. Zhang.

关键词： Optimization algorithms

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MagShadow: Physical Adversarial Example Attacks via Electromagnetic Injection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Ziwei Lin, Feng Ba, Zhongjie Lu, Li Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology China Institute of Blockchain and Data Security Hangzhou China

Physical adversarial examples (AEs) have become an increasing threat to deploying deep neural network (DNN) models in the real world. Popular approaches adopt sticking-based or projecting-based strategies that stick the printed adversarial patches to objects or directly project the AE onto objects. Although effective, these methods require access to target objects and generate visible artifacts, which reduces the attack's stealthiness. In this paper, we propose MagShadow, a new attack vector that leverages imperceptible electromagnetic (EM) signals to realize physical AEs. MagShadow utilizes the CCD camera sensor's susceptibility to EM injection attacks and induces fine-grained adversarial perturbations on the camera's captured image by injecting carefully-crafted signals with a low-cost portable device. As MagShadow directly manipulates the image sensor's output with EM signals, the attack requires no access to the target object and can keep stealthy. We study the feasibility of MagShadow in two typical DNN application scenarios (image classification and object detection) and design a framework to implement four different types of attacks, i.e., untargeted, targeted, hiding, and appearing attacks. Extensive real-world experiments on five different cameras are conducted, which demonstrate MagShadow's effectiveness against different popular DNN models (Inception v3, ResNet101, YOLO v3/v4). © 2004-2012 IEEE.

关键词： CCD cameras

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Association pattern-aware fusion for biological entity relationship prediction 24

Association pattern-aware fusion for biological entity relat...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lingxiang Jia Yuchen Ying Zunlei Feng Zipeng Zhong Shaolun Yao Jiacong Hu Mingjiang Duan Xingen Wang Jie Song Mingli Song State Key Laboratory of Blockchain and Data Security Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security State Key Laboratory of Blockchain and Data Security Zhejiang University and Bangsheng Technology Co Ltd.

ISBN: (纸本)9798331314385

Deep learning-based methods significantly advance the exploration of associations among triple-wise biological entities (e.g., drug-target protein-adverse reaction), thereby facilitating drug discovery and safeguarding human health. However, existing researches only focus on entity-centric information mapping and aggregation, neglecting the crucial role of potential association patterns among different entities. To address the above limitation, we propose a novel association pattern-aware fusion method for biological entity relationship prediction, which effectively integrates the related association pattern information into entity representation learning. Additionally, to enhance the missing information of the low-order message passing, we devise a bind-relation module that considers the strong bind of low-order entity associations. Extensive experiments conducted on three biological datasets quantitatively demonstrate that the proposed method achieves about 4%-23% hit@1 improvements compared with state-of-the-art baselines. Furthermore, the inter-pretability of association patterns is elucidated in detail, thus revealing the intrinsic biological mechanisms and promoting it to be deployed in real-world scenarios. Our data and code are available at https://***/hry98kki/PatternBERP.

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Threshold Huffman-Based Covert Timing Channel Detection

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International Journal of Network security 2025年第1期27卷 61-71页

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Huang, Xuwen School of Computer Science and Technology Huaqiao University Fujian Xiamen361000 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Fujian Xiamen361000 China

Network covert timing channels in computer networks evade effective detection by traditional security mechanisms such as firewalls, posing a significant threat to network security. However, most existing methods struggle to detect such channels when the window size is small. Additionally, the emergence of the new ϵ-κlibur channel has resulted in a decline in performance for detection methods such as GAS, SnapCatch, and DNN when detecting this particular channel. In this paper, we investigate a Huffman tree-based approach for detecting anomalous nodes in traffic and achieving the classification of channels through the design of weight intervals. Our method achieves an average detection rate of over 95% for each channel. Additionally, compared to GAS, SnapCatch, and DNN, our method improves the detection rates for the ϵ-κlibur channel by 11.75%, 8.46%, and 4.81%, respectively. The experimental results demonstrate that our proposed approach successfully detects ϵ-κlibur and achieves detection in a small window. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Computer system firewalls

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