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arXiv 2024年

作者： Liu, Decheng Su, Qixuan Peng, Chunlei Wang, Nannan Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the great development of generative model techniques, face forgery detection draws more and more attention in the related field. Researchers find that existing face forgery models are still vulnerable to adversarial examples with generated pixel perturbations in the global image. These generated adversarial samples still can't achieve satisfactory performance because of the high detectability. To address these problems, we propose an Adversarial Semantic Mask Attack framework (ASMA) which can generate adversarial examples with good transferability and invisibility. Specifically, we propose a novel adversarial semantic mask generative model, which can constrain generated perturbations in local semantic regions for good stealthiness. The designed adaptive semantic mask selection strategy can effectively leverage the class activation values of different semantic regions, and further ensure better attack transferability and stealthiness. Extensive experiments on the public face forgery dataset prove the proposed method achieves superior performance compared with several representative adversarial attack methods. The code is publicly available at https://***/clawerO-O/ASMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunjie Cai Zeqi Zheng Shanling Nie Yue Guo Ruijie Zhang Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai PR China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study’s 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly.

关键词： COVID-19 Deep learning Training Computed tomography Pulmonary diseases Lung Transformers

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PPBFL: A Privacy Protected Blockchain-based Federated Learning Model

arXiv

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arXiv 2024年

作者： Li, Yang Xia, Chunhe Lin, Wanshuang Wang, Tianbo The Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China The Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China The School of Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. However, attacks on model parameters and a lack of incentive mechanisms hinder the effectiveness of federated learning. Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training. Blockchain technology ensures the integrity of model parameters stored in the InterPlanetary File System (IPFS), providing protection against tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. This algorithm rewards nodes with greater computational power, promoting increased participation and effort in the federated learning process. A novel adaptive differential privacy algorithm is simultaneously applied to local and global models. This safeguards the privacy of local data at training clients, preventing malicious nodes from launching inference attacks. Additionally, it enhances the security of the global model, preventing potential security degradation resulting from the combination of numerous local models. The possibility of security degradation is derived from the composition theorem. By introducing reverse noise in the global model, a zero-bias estimate of differential privacy noise between local and global models is achieved. Furthermore, we propose a new mix transactions mechanism utilizing ring signature technology to better protect the identity privacy of local training clients. Security analysis and experimental results demonstrate that PPBFL, compared to baseline methods, not only exhibits superior model performance but also achieves higher security. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Instance and Category Supervision are Alternate Learners for Continual Learning

Instance and Category Supervision are Alternate Learners for...

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International Conference on computer Vision (ICCV)

作者： Xudong Tian Zhizhong Zhang Xin Tan Jun Liu Chengjie Wang Yanyun Qu Guannan Jiang Yuan Xie East China Normal University Shanghai Key Laboratory of Computer Software Testing & Evaluating Chongqing Institute of East China Normal University Tencent YouTu Lab Xiamen University Contemporary Amperex Technology Co. Limited

Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills. Yet, current CL algorithms tend to incur class-level forgetting as the label information is often quickly overwritten by new knowledge. This motivates attempts to mine instance-level discrimination by resorting to recent self-supervised learning (SSL) techniques. However, previous works have pointed out that the self-supervised learning objective is essentially a trade-off between invariance to distortion and preserving sample information, which seriously hinders the unleashing of instance-level *** this work, we reformulate SSL from the information-theoretic perspective by disentangling the goal of instance-level discrimination, and tackle the trade-off to promote compact representations with maximally preserved invariance to distortion. On this basis, we develop a novel alternate learning paradigm to enjoy the complementary merits of instance-level and category-level supervision, which yields improved robustness against forgetting and better adaptation to each task. To verify the proposed method, we conduct extensive experiments on four different benchmarks using both class-incremental and task-incremental settings, where the leap in performance and thorough ablation studies demonstrate the efficacy and efficiency of our modeling strategy.

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SketchCLIP: Text-based Attribute Manipulation for Face Sketch Synthesis

SketchCLIP: Text-based Attribute Manipulation for Face Sketc...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Mengdi Dong Chunlei Peng Decheng Liu Yu Zheng Nannan Wang Xinbo Gao State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665463959

This paper proposes a method of modifying the face sketch with text descriptions. Face sketch is widely used in the criminal field and digital entertainment field. Forensic painters usually draw face sketches based on descriptions provided by witnesses or clients. However, drawing a face sketch often takes lots of time and effort. Existing face sketch synthesis studies have not considered text-based sketch manipulation, and we find that applying text-driven editing methods on natural images directly to face sketches causes severe distortion of generated results. Therefore, this paper proposes a novel text-based attribute manipulation method for face sketch synthesis, named SketchCLIP. Our approach adopts text-driven attribute manipulation by using the powerful Contrastive Language-Image Pre-Training (CLIP) model, which not only conforms to the current drawing process of face sketches but also does not require tedious manual operations and allows for more diverse modifications. Besides, we design an intra-modality fine-tuning module to eliminate distortion and improve the quality of the modified face sketch. Through extensive comparison experiments on public face sketch datasets, our method is demonstrated to be very excellent in the effectiveness of the face sketch processing and the quality of modified results.

关键词： Measurement Image quality Costs Biometrics (access control) Forensics Entertainment industry Distortion

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Load Balancing Optimization for Transformer in Distributed Environment

Load Balancing Optimization for Transformer in Distributed E...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Delu Ma Zhou Lei Shengbo Chen Peng Wang School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9781665408790

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. Therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. Distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. The results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy.

关键词： Training Computational modeling Machine learning Load management Transformers Servers Distributed computing

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CHESS: Learning User Correlation from Semantic-related Cascades for Diffusion Prediction

CHESS: Learning User Correlation from Semantic-related Casca...

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Smart World Congress (SWC), IEEE

作者： Huacheng Li Chunhe Xia Tianbo Wang Haopeng Zhao Key Laboratory of Beijing Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Understanding the information diffusion trajectory in online social networks has become an ever-growing prominent research direction in the information era. Users interact with social information they are interested in, and cascades record diffusion trajectories of massive social information. It implies two characteristics of diffusion cascades: topic sharing among different cascades and infected user correlation within the same cascades. However, previous works ignore these two characteristics and primarily focus on whether each infected user is correctly predicted individually, which leads to learning biased diffusion models under the data-driven mode. To address this problem, we propose a novel architecture to exploit such latent user associations among topic-related cascades. First, we leverage the hypergraph and heterogeneous graph to exploit infected user correlations jointly and generate user feature vectors. Second, we devise an enhanced cascade similarity loss function to evaluate prediction results from delay (horizontal) and rank (vertical) scopes. Furthermore, we introduce the extra predictor to alleviate the interference with the primary model. Experiments on four public datasets validate the effectiveness of our proposed method and significantly outperform existing models on hits@κ and map@κ indicators.

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CRCS: Learning Synergistic Cascade Correlation for Microscopic Cascade Prediction

CRCS: Learning Synergistic Cascade Correlation for Microscop...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Huacheng Li Chunhe Xia Tianbo Wang Haopeng Zhao Key Laboratory of Beijing Network Technology Beihang University Beijing China Key Laboratory of Beijing Network Technology Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Information diffusion prediction is the basis of many fundamental tasks, such as social recommendation and community detection. Currently, most researchers infer user correlation based on cascade records to predict future infected users. However, they only consider whether the infected users at each location are correctly predicted, ignoring the semantic differences between users who participate in the retweets of similar content and other users. This naturally leads to learning biased user features. In this paper, we compute semantic relevance between two different cascades (e.g., c m and c n ) based on user coincidence and develop the cascade correlation graph (CCG) to learn user features. Afterward, we devise a position-independent sequence similarity loss (SSL) function combined with an auxiliary predictor to optimize the user encoder. The above two methods make users participating in the relevant cascades more relevant than others. Experiments show that the two methods are effective, and the combination of them can improve performance more significantly.

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CFC: a Cascade Forest approach to discover Cancer driver genes using multi-omics data

CFC: a Cascade Forest approach to discover Cancer driver gen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Zhang Yijing Yang Zhe Wang Dongdong Li Jingping Liu Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science University of Illinois Urbana-Champaign Champaign Illinois United States of America

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

With the development of next-generation sequencing technology, massive genomic data has been generated, primarily encouraging research on cancer driver genes. Many bioinformatics methods were proposed to identify driver genes. However, the results of driver gene identification a mong these methods show considerable differences. It is still challenging to obtain a comprehensive catalog of cancer drivers. Although current methods have greatly promoted the development of driver genes, few methods can integrate the identification results of existing methods. To solve such problems in cancer driver genes research, we proposed a cascade forest model to discover cancer driver genes(CFC) that can integrate multi-omics data and annotation scores from different cancer driver gene identification algorithms. The proposed method got precise results for 33 cancer types and Pan-cancer. The CFC framework identified 275 driver genes in Pan-cancer, of which 179 were included in the Gold standard. The identified genes were enriched i n t he principal cancer signaling pathways.

关键词： Sequential analysis Annotations Genomics Forestry Data models Bioinformatics Next generation networking

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Test Sequence Generation Based on Chain Unique Input-Output Sequence in FSM Conformance testing

Test Sequence Generation Based on Chain Unique Input-Output ...

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作者： Weiwei Lin Hongwei Zeng School of Computer Engineering and Science Shanghai UniversityShanghai Key Laboratory of Computer Software Evaluating &Testing School of Computer Engineering and Science Shanghai University

This paper introduces a test method based on chain unique input/output(CUIO) sequence in finite state machine(FSM) conformance testing. For FSMs with UIO for each state, the test sequence based on CUIO can identify all the states and verify all the transitions. For FSMs without UIO for some states, the test sequence based on CUIO can identify as many states and verify as many transitions as possible. Meanwhile, W method is introduced to deal with the unidentified states and the unverified transitions. For FSMs without UIO for each state, the method completely degenerates to W method. It is proved by experiment that the average and maximum reduction rate are 45.5% and 63.2% comparing with UIO ***, the method is always feasible even when UIO method fails.

关键词： Test Sequence Generation Based on Chain Unique Input-Output Sequence in FSM Conformance testing

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