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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Semi-automatic post-processing of multi-view 2D-plus-depth video 30

Semi-automatic post-processing of multi-view 2D-plus-depth v...

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30th Stereoscopic Displays and Applications Conference, SD and A 2019

作者： Sespede, Braulio Seitner, Florian Gelautz, Margrit Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Emotion3D GmbH Vienna Austria

We propose a post-processing framework based on multi-view interactive video segmentation for correcting 2D-plus-depth video footage. The suggested approach uses user-made scribbles to guide the multi-view segmentation process, which is based on an efficient cost-volume filtering algorithm. We extend the 2D algorithm to 3D and propose several improvements that increase precision and recall while also decreasing the need for user input. Our semi-automatic approach is supported by an interactive visualization tool that integrates both 2D and 3D views of the footage, allowing the user to explore novel views coherently and grasp a better understanding of the underlying data. We integrate our post-processing framework into a workflow for generating dynamic meshes from footage recorded by multiple stereo cameras, demonstrating the applicability of the technique. © 2019, Society for Imaging Science and technology.

关键词： Stereo image processing

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CapST: An Enhanced and Lightweight Method for Deepfake Video Classification

arXiv

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arXiv 2023年

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Shahzad, Sahibzada Adil Institute of Information Science Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei116 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Dire Dawa University Ethiopia Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

The proliferation of deepfake videos, synthetic media produced through advanced Artificial Intelligence techniques, has raised significant concerns across various sectors, encompassing realms such as politics, entertainment, and security. In response, this research introduces an innovative and streamlined model designed to classify deepfake videos generated by five distinct encoders adeptly. Our approach not only achieves state-of-the-art performance but also optimizes computational resources. At its core, our solution employs part of a VGG19_bn as a backbone to efficiently extract features, a strategy proven effective in image-related tasks. We integrate a Capsule Network coupled with a Spatial-Temporal attention mechanism to bolster the model’s classification capabilities while conserving resources. This combination captures intricate hierarchies among features, facilitating robust identification of deepfake attributes. Delving into the intricacies of our innovation, we introduce an existing video-level fusion technique that artfully capitalizes on temporal attention mechanisms. This mechanism serves to handle concatenated feature vectors, capitalizing on the intrinsic temporal dependencies embedded within deepfake videos. By aggregating insights across frames, our model gains a holistic comprehension of video content, resulting in more precise predictions. Experimental results on an extensive benchmark dataset of deepfake videos (DFDM) showcase the efficacy of our proposed method. Notably, our approach achieves up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models, all while demanding fewer computational resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

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Realistic shrinking city layouts

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Computers and Graphics (Pergamon) 2020年 86卷 15-26页

作者： Pueyo, Oriol Sabrià, Albert Pueyo, Xavier Patow, Gustavo Wimmer, Michael University of Girona. ViRVIG. Dept. IMAE GironaMontilivi 17003 Spain TU Wien. Institute of Visual Computing and Human-Centered Technology Austria

One important use of realistic city environments is in the video game industry. When a company works on a game whose action occurs in a real-world environment, a team of designers usually creates a simplified model of the real city. In particular, the resulting city is desired to be smaller in extent to increase playability and fun, avoiding long walks and "boring" neighborhoods. This is manual work, usually started from scratch, where the first step is to take the original city map as input, and from it create the street network of the final city, removing insignificant streets and bringing important places closer together in the process. This first draft of the city street network is like a kind of skeleton with the most important places connected, from which the artist can (and should) start working until the desired result is obtained. In this paper, we propose a solution to automatically generate such a first simplified street network draft. This is achieved by using the well-established seam-carving technique applied to a skeleton of the city layout, built with the important landmarks and streets of the city. The output that our process provides is a street network that reduces the city area as much as the designer wants, preserving landmarks and key streets, while keeping the relative positions between them. For this, we run a shrinking process that reduces the area in an irregular way, prioritizing the removal of areas of less importance. This way, we achieve a smaller city but retain the essence of the real-world one. To further help the designer, we also present an automatic filling algorithm that adds unimportant streets to the shrunken skeleton. © 2019 Elsevier Ltd

关键词： Musculoskeletal system

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Unmasking Illusions: Understanding human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

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

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, human Factors and Perceptual Insights

arXiv

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arXiv 2024年

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

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

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AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection

arXiv

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arXiv 2023年

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

Multimodal manipulations (also known as audiovisual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multi-modal models that can exploit both pieces of information simultaneously. Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection. This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multi-modal video forgery detection. We use the transformer-based SSL pre-trained Audio-visual HuBERT (AV-HuBERT) model as a visual and acoustic feature extractor and a multi-scale temporal convolutional neural network to capture the temporal correlation between the audio and visual modalities. Since AV-HuBERT only extracts visual features from the lip region, we also adopt another transformer-based video model to exploit facial features and capture spatial and temporal artifacts caused during the deepfake generation process. Experimental results show that our model outperforms all existing models and achieves new state-of-the-art performance on the FakeAVCeleb and DeepfakeTIMIT datasets. © 2023, CC BY.

关键词： Audio acoustics

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Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation

Deep Sensor Fusion with Pyramid Fusion Networks for 3D Seman...

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IEEE Symposium on Intelligent Vehicle

作者： Hannah Schieber Fabian Duerr Torsten Schoen Jü rgen Beyerer Human-Centered Computing and Extended Reality Friedrich-Alexander University (FAU) Erlangen-N&#x00FC rnberg Erlangen Germany Vision and Fusion Laboratory Karlsruhe Institute of Technology Karlsruhe Germany Research Institute Almotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation (IOSB) Fraunhofer Center of Machine Learning Karlsruhe Germany

Robust environment perception for autonomous vehicles is a tremendous challenge, which makes a diverse sensor set with e.g. camera, lidar and radar crucial. In the process of understanding the recorded sensor data, 3D semantic segmentation plays an important role. Therefore, this work presents a pyramid-based deep fusion architecture for lidar and camera to improve 3D semantic segmentation of traffic scenes. Individual sensor backbones extract feature maps of camera images and lidar point clouds. A novel Pyramid Fusion Backbone fuses these feature maps at different scales and combines the multimodal features in a feature pyramid to compute valuable multimodal, multi-scale features. The Pyramid Fusion Head aggregates these pyramid features and further refines them in a late fusion step, incorporating the final features of the sensor backbones. The approach is evaluated on two challenging outdoor datasets and different fusion strategies and setups are investigated. It outperforms recent range view based lidar approaches as well as all so far proposed fusion strategies and architectures.

关键词： Point cloud compression Image segmentation Laser radar Three-dimensional displays Head Fuses Semantics

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Person Localisation under Fragmented Occlusion*

Person Localisation under Fragmented Occlusion*

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Roman Pflugfelder Jonas Auer Center of Digital Safety & Security Giefinggasse 4 AIT Austrian Institute of Technology Vienna Austria TU Wien Inst. of Visual Comp. & Human-Centered Technology Favoritenstr. 9/193-1 Vienna Austria

ISBN: (纸本)9781665433976

Occlusion is a fundamental challenge in object recognition. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs in natural environments such as forests. Less is known in computer vision about fragmented occlusion and object recognition. Interestingly, human vision has far more explored this problem as the human visual system evolved to fragmented occlusion at the times when mankind occupied rainforest. A motivating example of fragmented occlusion is object detection through foliage which is an essential requirement in green border surveillance. Instead of detection, this paper studies the simpler problem of localisation with persons. A neural network based method shows a precision larger than 90% on new image sequences capturing the problem. This is possible by two observations: (i) fragmented occlusion is unsolvable in single images without temporal information, and (ii) colour quantisation and colour swapping is essential to force the training of the network to learn from the available temporal information in the spatiotemporal data.

关键词： Training visualization Image color analysis Surveillance Vegetation mapping Detectors visual systems

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