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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Relation extraction using TBL with distant supervision 4

Relation extraction using TBL with distant supervision

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4th Joint International Semantic Technology Conference, JIST-WP 2014 - Co-located with the 4th Joint International Semantic Technology Conference, JIST 2014

作者： Choi, Maengsik Kim, Harksoo Program of Computer and Communications Engineering College of IT Kangwon National University Korea Republic of

Supervised machine learning methods have been widely used in rela- tion extraction that finds the relation between two named entities in a sentence. However, their disadvantages are that constructing training data is a cost and time consuming job, and the machine learning system is dependent on the do- main of the training data. To overcome these disadvantages, we construct a weakly labeled data set using distant supervision and propose a relation extrac- tion system using a transformation-based learning (TBL) method. This model showed a high F1-measure (86.57%) for the test data collected using distant su- pervision but a low F1-measure (81.93%) for gold label, due to errors in the training data collected by the distant supervision method.

关键词： Supervised learning

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Re-ranking ASR outputs for spoken sentence retrieval 4

Re-ranking ASR outputs for spoken sentence retrieval

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4th Joint International Semantic Technology Conference, JIST-WP 2014 - Co-located with the 4th Joint International Semantic Technology Conference, JIST 2014

作者： Song, Yeongkil Ahn, Hyeokju Kim, Harksoo Program of Computer and Communications Engineering College of IT Kangwon National University Korea Republic of

In spoken information retrieval, users' spoken queries are converted into text queries by using ASR engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a docu- ment collection is a small set of short texts, the errors will more affect the per- formances of information retrieval systems. To improve the top-1 accuracies of the ASR engines, we propose a post-processing model to rearrange top-n out- puts of ASR engines by using Ranking SVM. To improve the re-ranking per- formances, the proposed model uses various features such as ASR ranking in- formation, morphological information, and domain-specific lexical information. In the experiments, the proposed model showed the higher precision of 4.4% and the higher recall rate of 6.4% than the baseline model without any post- processing. Based on this experimental result, the proposed model showed that it can be used as a post-processor for improving the performance of a spoken information retrieval system if a document collection is a restricted amount of sentences.

关键词： Information retrieval systems

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Multi-factor Relay-Node Selection Algorithm for IoV in 3D Mountain Scenarios 15th

Multi-factor Relay-Node Selection Algorithm for IoV in 3D Mo...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Liao, Huicong Liu, Qinghua Wang, Jin Wang, Jing Cao, Dun Program of Computer and Communications Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9789819724468

In Internet of Vehicles, a multi-factor weighted relay-node selection method is proposed to address the problem that single-hop relay node performance is optimal but its multi-hop is not optimal, which exists on the greedy algorithm in 3D mountain scenarios. The ration for the algorithm is demonstrated by conducting a comprehensive analysis of the impact of 3D mountain features on the relay-node selection method. The experimental results in simulations on real-world map show that the designed algorithm performs better compared with the improved greedy algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Landforms

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Lexical feature extraction method for classification of erroneous online customer reviews based on pattern matching

Lexical feature extraction method for classification of erro...

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4th International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013

作者： Choi, Maengsik Shin, Junsoo Kim, Harksoo Program of Computer and Communications Engineering College of Information Technology Kangwon National University Chuncheon-si Korea Republic of

ISBN: (纸本)9783642406744

In morpheme-based languages such as Korean and Japanese, spacing and spelling errors that frequently occur in online documents make it difficult to reliably extract informative lexical clues for sentiment analysis. To overcome this problem, we propose a simple, reliable lexical feature extraction method for sentiment classification systems;this method targets online customer reviews in Korean, which include numerous spacing and spelling errors. The proposed method performs longest-matching between input sentences and two kinds of patterns (spacing-unit patterns and phoneme patterns) that are automatically constructed from a large POS tagged corpus. Thereafter, the method returns content words associated with the longest matched patterns. In the experiments on sentiment classification, the proposed method outperformed previous lexical feature extraction methods, which are based on conventional morphological analyzers. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Pattern matching

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Mathematical document retrieval model using structural information of equations in pseudo-documents

Mathematical document retrieval model using structural infor...

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4th International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013

作者： Song, Yeongkil Shin, Junsoo Kim, Harksoo Program of Computer and Communications Engineering College of Information Technology Kangwon National University Chuncheon-si Korea Republic of

ISBN: (纸本)9783642406744

Math-aware search engines are required to effectively retrieve mathematical documents including various equations. In this paper, we propose a mathematical document retrieval system by which users can retrieve documents using any combination of keywords and equations. The proposed system indexes equations and their surrounding keywords from mathematical documents. Then, it searches and ranks mathematical documents using a language model modified for the heterogeneous indexing units (i.e., mixtures of equations and keywords). In the experiments, the proposed system performed well, especially for high ranks. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Search engines

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Learn2Evade: Learning-based generative model for evading PDF malware classifiers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第4期2卷 299-313页

作者： Bae, Ho Lee, Younghan Kim, Yohan Hwang, Uiwon Yoon, Sungroh Paek, Yunheung Department of Cyber Security EwhaWomans University Seoul03760 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Institute of NewMedia and Communications Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware *** deem that our problem is more challenging than those againstMLmodels for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yetmaintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost 13, Hidost 16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-The-Art PDF malware classifiers and raises a serious security concern in the presence of adversaries. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Generative adversarial networks

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Leveling up by design: Games design + technical/engineering communication = innovative games and high-functioning teams 46

Leveling up by design: Games design + technical/engineering ...

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46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for engineering Education Excellence

作者： Nathans-Kelly, T. White, W. Engineering Communications Program College of Engineering Cornell University IthacaNY United States Computer Science College of Engineering Cornell University IthacaNY United States

ISBN: (纸本)9782873520168

Spring semester of 2018 marks the eleventh anniversary of our integration of writing with game development. Agile and continuous development require that we (as instructors) revisit and revise the courses each year. The core feature of our approach is to make sure that the documentation always moves the development process forward, and CATME assists in our continuous assessment efforts. For all documents in the process, we identify for our students exactly why it is important to the agile cycles;we cut documents that don't contribute to the process. By applying these simple rules, we believe that instructors can create highly effective and professional teams that are better prepared to meet demanding early careers in CS or related fields. © Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for engineering Education Excellence. All rights reserved.

关键词： engineering education

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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