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DBIA: data-FREE BACKDOOR ATTACK AGAINST TRANSFORMER NETWORKS

DBIA: DATA-FREE BACKDOOR ATTACK AGAINST TRANSFORMER NETWORKS

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IEEE international conference on Multimedia and Expo (ICME)

作者： Lv, Peizhuo Ma, Hualong Zhou, Jiachen Liang, Ruigang Chen, Kai Zhang, Shengzhi Yang, Yunfei Chinese Acad Sci SKLOIS Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Boston Univ Dept Comp Sci Metropolitan Coll Boston MA USA

ISBN: (纸本)9781665468916

Recently, transformer architecture has demonstrated its significance in both Natural Language processing (NLP) and Computer Vision (CV) tasks. Although other network models are known to be vulnerable to the backdoor attack, which embeds triggers in the models and controls the models' behavior when the triggers are presented, little is known about how such an attack performs on the transformer models. In this paper, we propose DBIA, a novel data-free(1) Backdoor Attack against the CV-oriented transformer networks, leveraging the inherent attention mechanism of transformers to generate triggers and injecting the backdoor using a poisoned substitute dataset. We conducted extensive experiments using three benchmark transformers, i.e., ViT, DeiT, and Swin Transformer, on four mainstream image classification tasks, i.e., ImageNet, CIFAR-10, GTSRB, and Youtube Face. The evaluation results demonstrate that, with fewer resources, our approach can embed backdoors with a high success rate and a low impact on the performance of the victim transformers.

关键词： Computer vision

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Handwriting Curve Interpolation Using Gradient Graph Laplacian Regularizer

Handwriting Curve Interpolation Using Gradient Graph Laplaci...

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IEEE international conference on Multimedia and Expo (ICME)

作者： Lin, Yinhe Chen, Fei Cheng, Hang Wang, Meiqing Fuzhou Univ Coll Math & Stat Fuzhou Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

ISBN: (纸本)9781665468916

Due to the technical limitation of pen tablets, there are sensing points data loss from the touch screen when the handwriting speed is fast. This problem will cause discrete, segmented, and unsmooth handwriting curves. In order to recover the unknown point coordinates from the observed corrupted curve of handwriting, we propose a curve interpolation algorithm by combining gradient graph Laplacian regularizer and cyclic shift. We first define the gradient of 2D curve and create the related gradient graph. Then the handwriting curve is interpolated by the gradient graph Laplacian regularizer. For handwriting stroke offset, we introduce a cyclic shift of handwriting for translation invariance. Experimental results on synthetic curves and handwriting datasets show that the interpolation quality of our proposed algorithm is better than other competing algorithms, and it promotes the curve smoothness of the turning points.

关键词： Graph signal processing curve interpolation gradient graph Laplacian regularizer cyclic shift

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MalDetect: Malware Classification Using API Sequence and Comparison with Transformer Encoder 17

MalDetect: Malware Classification Using API Sequence and Com...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart data, Smartdata 2024

作者： Lin, Jingjing Lin, Jingsong Lyu, Chenxi Fan, Xinmin Dong, Chen Fuzhou University College of Computer and Data Science Fuzhou China Fujian Normal University Network and Data Center Fuzhou China

ISBN: (纸本)9798350351637

With each advancement in internet technology, new security challenges arise. The prevalence of malicious programs continues to increase, which makes it crucial to detect and address them effectively. Many researchers focus on solving different datasets by using deep learning methods and make significant progress. However, these strategies must be continuously improved to adapt to the latest data. In this paper, an improved model based on CNN-LSTM is proposed to detect and classify malware programs, named malDetect I. At the same time, the Transformer Encoder module is also modified based on model Bert to adapt to the classification task. Lastly, two models are compared with prediction results on evaluation indicators. The data used in this paper is the Windows API sequence extracted after dynamic operation. The text processing methods are also suitable for processing sequence data. The experiment uses Word2Vec and two different learning rate strategies, and the improved model accuracy is 9.83% higher than the original CNN-LSTM model. The model integrated the BiLSTM model with the Self-Attention mechanism, named malDetect II, is 11.46% higher than the basic model CNN-LSTM and 2.82% higher than the Transformer Encoder classification model. © 2024 IEEE.

关键词： Deep learning

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Long-Term Vibrational Monitoring of the Exedra of Marcus Aurelius' Hall 10th

Long-Term Vibrational Monitoring of the Exedra of Marcus Aur...

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10th international Operational Modal Analysis conference (IOMAC)

作者： Rinaldi, Cecilia Crognale, Marianna Ciambella, Jacopo Potenza, Francesco Gattulli, Vincenzo Sapienza Univ Rome Dept Struct & Geotech Engn Via Eudossiana 18 I-00184 Rome Italy Univ G dAnnunzio Dept Engn & Geol V le Pindaro 42 I-65127 Pescara Italy

ISBN: (纸本)9783031614231;9783031614217;9783031614200

In today's technology-driven era, especially within Information and Communication Technologies, a vast reserve of data is valuable for preserving cultural heritage. Integrating software, hardware, and communication technology, exemplified by the concept of "Digital Twin", offers innovative measurement acquisition and processing. Vibration tests, notably ambient vibration testing, have become a cornerstone for understanding structural health and dynamics due to their simplicity and affordability. Careful data processing reveals critical modal features like frequencies, shapes, and dampings, serving as vital indicators for assessing structural health during normal operation. For long-term monitoring, automating structural identification reduces errors, with a focus on temporal modal property variations and environmental influences. This research activity focuses on developing a framework for automated detection of the natural frequencies of the Marcus Aurelius Exedra within the Capitoline Museum of Rome, (Italy). The research aims to establish an effective and continuous structural health monitoring approach that utilizes automated techniques to track changes in dynamic characteristics over an extended period. The study includes the design of the monitoring system, data collection, and the implementation of automated frequency tracking algorithms. A continuous long-term monitoring system, composed of six triaxial MEMS non-commercial accelerometers, has been in operation since July 2023. The identification of the primary structural features within the Exedra hall is accomplished through an automated procedure that integrates advanced methodologies, including Stochastic Subspace Identification and data clustering techniques. Results demonstrate the system effectiveness in continuously assessing structural dynamics, offering a proactive approach to structural health monitoring.

关键词： Continuous Structural Monitoring data Clustering Frequency Tracking Operational Modal analysis Digital Twin

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Using Modified Adult Speech as data Augmentation for Child Speech Recognition 48

Using Modified Adult Speech as Data Augmentation for Child S...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Fan, Zijian Cao, Xinwei Salvi, Giampiero Svendsen, Torbjørn Norwegian University of Science and Technology Department of Electronic Systems Norway

ISBN: (纸本)9781728163277

data augmentation is a technique which enhances the size and quality of training data such that deep learning or machine learning models can achieve better performance. This paper proposes a novel way of applying data augmentation for child speech recognition in the low data resource scenario. data augmentation is achieved by modifying existing adult speech signals. The procedure consists of two main parts, resampling, and time scaling. The experiment involves both speech from children aged from kindergarten to grade 10, and adults' speech. We test the proposed method using both a TDNN-HMM and a GMM-HMM acoustic model. The results show that the proposed data augmentation scheme achieves a relative 7.95% reduction of WERs compared with 4.56% relative reduction when using a traditional bilinear frequency warping approach. © 2023 IEEE.

关键词： children's speech recognition data augmentation GMM-HMM TDNN-HMM

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Text Mining data Based Summarization of Novel Approach for Covid-19 with the Use of Machine Learning Techniques 5

Text Mining Data Based Summarization of Novel Approach for C...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lakshmi, Chataparti Suvarna Saxena, Sameer Kumar, Billakurthi Suresh Amity University Amity Institute of Information Technology Rajasthan Jaipur India

ISBN: (纸本)9798350383522

At the start of the year 2020, the World Health Organization (WHO) officially recognized COVID-19 as a novel pandemic and subsequently released a corresponding declaration. The aforementioned lethal virus had the capacity to disseminate and proliferate across many countries worldwide. Throughout the duration of the pandemic, social media platforms like as Twitter played a pivotal role in generating considerable amounts of data that contributed to enhancing the efficacy of healthcare-related decision-making processes. Consequently, we propose that the viewpoint articulated by users might be examined by using effective Supervised Machine Learning (SML) algorithms for the purpose of predicting the onset of ailments and providing timely alerts. This study presents a text mining classifier that use machine learning approaches to create summary text. Upon gathering the tweets, we proceeded to prepare them for preprocessing and assign class labels to each instance, including categories such as right, wrong, and neutral, among others. During the second step, a variety of commonly used techniques, including TF-IDF, co-relational analysis, Natural Language processing (NLP), and relational dependency analysis, are utilized to extract many features from the text. These features are then used to construct the feature vector. For the classification module, we used a binary classification method together with five machine learning algorithms to assess the effectiveness of the suggested model. The NLP-SVM model has a classification accuracy of 95.10%, while the TFIDF-SVM model achieves a classification accuracy of 93.50%. This finding provides evidence that the suggested model is efficacious in the categorization of extensive textual data pertaining to COVID-19, namely on twitter data. © 2024 IEEE.

关键词： classification COVID-19 Feature extraction NLP sentiment analysis social media twitter data

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The Integration of Deep Learning Techniques and Big data Analytics for Improved Breast Cancer Diagnosis and Treatment: A Systematic Review

The Integration of Deep Learning Techniques and Big Data Ana...

引用

2024 international conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Gebre, Hambisa Mitiku Wegari, Getachew Mamo Jimma University Faculty of Computing and Informatics Department of Information Science Jimma Ethiopia Jimma University Faculty of Computing and Informatics Department of Information Technology Jimma Ethiopia

ISBN: (纸本)9798331527792

Background:- Breast cancer is one of the most common and lethal diseases in the world. Traditional breast cancer diagnostic and prognosis procedures usually involve significant human talent and can be time-consuming and subjective, leading to potential errors and treatment delays. Two recent technical advancements, deep learning, and big data analytics are promising to improve breast cancer diagnosis and therapy. As a result, this systematic review aims to examine specific papers that describe deep learning techniques and big data analytics in the context of breast cancer diagnosis and ***: Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), peer-reviewed articles published in the English language from January 2020 to August 2023 were selected from electronic databases such as PubMed, ACM, Digital Library, and Science Direct, as well as citations and manual searches. This review paper takes into account papers about deep learning algorithms, big data analytics, the efficacy of deep learning and big data analytics, as well as the problems and limitations of merging deep learning methods with big data analytics. Articles that were not original or in English were not ***: - Ten articles were identified for this review. The finding showed that deep-learning techniques play a great role in analyzing vast datasets to identify malignant cells or tumors, aiding radiologists in accurate diagnoses and improving patient outcomes. Big data analytics in breast cancer diagnosis and treatment can improve accuracy, efficiency, and patient-centered care. Deep learning techniques are utilized with big data, enhancing screening test accuracy and guiding diagnostic procedures, especially for image-based scanning that leads to early breast cancer identification, improving patient outcomes, and potentially enhancing diagnostics. Obtaining huge volumes of high-quality data for training deep learning models is a challenge and limita

关键词： big data analytics breast cancer diagnosis and treatment Deep learning algorithms

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Research on Sailboat Price Prediction Based on LS-SVM Prediction Algorithm and XGBoost Algorithm

Research on Sailboat Price Prediction Based on LS-SVM Predic...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Luhan Peng Qingqing Zhang College of Architecture and Environment Sichuan University Chengdu China School of Chemical Engineering Sichuan University Chengdu China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper investigates the prediction of sailboat prices using the LS-SVM prediction algorithm and the XGBoost algorithm. The study involves training prediction models, determining coefficients of determination, and calculating the mean absolute error (MAE) for both LS-SVM and SVM algorithms, resulting in values of 0.99143, 0.89572, and 0.94512, respectively. The LS-SVM model demonstrated the highest predictive accuracy. Additionally, a regional impact model incorporating the XGBoost algorithm was introduced. data from each region was utilized to predict the performance of second-hand sailboats, serving as the training set for the model. The study culminated in the derivation of the solution model through the XGBoost algorithm.

关键词： Training Support vector machines Accuracy Biological system modeling Heuristic algorithms Computational modeling Pricing Predictive models Prediction algorithms data models

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MAID: Model Attribution via Inverse Diffusion

MAID: Model Attribution via Inverse Diffusion

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhu, Luyu Ye, Kai Yao, Jiayu Li, Chenxi Zhao, Luwen Cao, Yuxin Wang, Derui Hao, Jie Beijing University of Posts and Telecommunications China The University of Hong Kong Hong Kong National University of Singapore Singapore CSIRO's Data61 Australia

ISBN: (纸本)9798350368741

The surge in AI-generated images, blending authentic and synthetic content, raises security concerns and complicates model attribution, especially with limited transparency. Existing methods either struggle to attribute across multiple frameworks or rely on additional conditions, such as textual descriptions and white-box access to the source model, limiting their practicality and effectiveness in real-world scenarios. To address this gap, we introduce Model Attribution via Inverse Diffusion (MAID), the first framework-agnostic and self-sufficient approach that leverages the source model features extracted by diffusion models, which also works for images generated from GANs. By employing the inverse diffusion process, we are able to utilize pre-trained Diffusion models as Denoising Autoencoders, mapping images into a latent space and extracting the Diffusion Model Activations (DMA). This mapping effectively captures the unique characteristics of images originating from different source models, including authentic images, which showcase distinct latent Gaussian signatures. Experimental results show that, even in data-asymmetric unfair comparisons, the attribution classifier trained with our proposed DMA achieves approximately 15% and 3% higher ACC compared to SOTA methods on the DiffusionForensics and Artifact datasets, respectively. The code is available at https://***/Zhu-Luyu/MAID. © 2025 IEEE.

关键词： Deepfake Diffusion Model Model Attribution

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GLM's in data Science as a Tool in the Prediction of Delirium 3rd

GLM's in Data Science as a Tool in the Prediction of Deliriu...

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3rd international conference on Optimization, Learning algorithms and Applications (OL2A)

作者： Coelho, Alexandra Braga, Ana Cristina Mariz, Jose Univ Minho Sch Engn P-4710057 Braga Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal ICVS Life & Hlth Sci Res Inst P-4700057 Braga Portugal

ISBN: (纸本)9783031530241;9783031530258

Delirium is an acute neuropsychiatric dysfunction, prevalent in patients admitted to hospitals for inpatient and intensive care, being clinically characterized by attention deficit and clouding of the state of consciousness associated with cognitive disorders. Since this is a multifactorial manifestation that develops over a short period of time, it is usually underdiagnosed and neglected. Consequently, this disorder appears associated with high rates of mortality and morbidity, leading to a longer period of hospitalization. Additionally, delirium can be categorized, according to the motor activity profile, into two subtypes: hypo and hyperactive. Currently, there are studies and assessment instruments for the study and prediction of the disease at an earlier stage, based on a set of risk factors. However, in the context of a hospital emergency, time is essential, as well as a correct and early diagnosis to intervene as quickly as possible. In this context, the goal of this paper arises, which aims to implement a diversity of techniques to preprocess the data to perform the multinomial logistic regression. The ultimate goal is to identify the most effective data balancing technique for accurate prediction of delirium occurrence and its subtypes, based on the methodology of generalized linear models (GLMs), specifically multinomial logistic regression (MLR).

关键词： delirium Generalized linear model Multinomial logistic regression

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