Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (7)

Search Parameters:
Keywords = YouTube video identification

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 463 KiB  
Article
Interpretable Identification of Dynamic Adaptive Streaming over HTTP (DASH) Flows Based on Feature Engineering
by Arkadiusz Biernacki
Appl. Sci. 2025, 15(5), 2253; https://doi.org/10.3390/app15052253 - 20 Feb 2025
Viewed by 617
Abstract
Internet service providers allocate network resources for different network flows. Among them, video streaming requires substantial network bandwidth to provide a satisfactory user experience. The identification of video traffic is one of the tools that helps to manage and optimise network resources. However, [...] Read more.
Internet service providers allocate network resources for different network flows. Among them, video streaming requires substantial network bandwidth to provide a satisfactory user experience. The identification of video traffic is one of the tools that helps to manage and optimise network resources. However, available solutions usually focus on traffic traces from a single application and use black-box models for identification, which require labels for training. To address this issue, we proposed an unsupervised machine learning model to identify traffic generated by video applications from the three popular services, namely YouTube, Netflix, and Amazon Prime. Our methodology involves feature generation, filtering, and clustering. The clustering used the most significant features to group similar traffic patterns. We employed the following three algorithms that represent different clustering methodologies: partition-based, density-based, and probabilistic approaches. The clustering achieved precision between 0.78 and 0.93, while recall rates ranged from 0.68 to 0.84, depending on the experiment parameters, which is comparable with black-box learning models. The model presented is interpretable and scalable, which is useful for its practical application. Full article
(This article belongs to the Special Issue AI Tools and Methods for Computer Networks)
Show Figures

Figure 1

17 pages, 898 KiB  
Article
Study of the Correlation between Streaming Video Platform Content on Food Production Processes and the Behavioral Intentions of Generation Z
by Xi-Yu Zhang, Ching-Tzu Chao, Yi-Tse Chiu and Han-Shen Chen
Foods 2024, 13(10), 1537; https://doi.org/10.3390/foods13101537 - 15 May 2024
Cited by 4 | Viewed by 2339
Abstract
With an estimated 2.4 million cases of foodborne illnesses recorded annually in the UK alone, food safety has become a paramount concern among stakeholders. Modern technology has positioned streaming platforms as pivotal conduits for disseminating information. Channels such as YouTube offer detailed recordings [...] Read more.
With an estimated 2.4 million cases of foodborne illnesses recorded annually in the UK alone, food safety has become a paramount concern among stakeholders. Modern technology has positioned streaming platforms as pivotal conduits for disseminating information. Channels such as YouTube offer detailed recordings of the food production process, granting consumers extensive visibility of the food journey from farm to table. This increased transparency not only promotes vigilant monitoring of food safety practices but also solicits consumer feedback regarding the public exposure to food processing videos. Based on the Theory of Planned Behavior (TPB), this study augments its framework with constructs, such as perceived trust, perceived risk, community experience, and brand identity, to evaluate Taiwan’s Generation Z consumer behavioral intentions. With 226 valid responses amassed, structural equation modeling facilitated elucidation of the relationships among the constructs. This analysis yielded three salient insights. First, Generation Z’s engagement with food processing videos on streaming platforms is positively correlated with their subsequent purchasing behavior. Second, enriched community experience was correlated with strengthened brand identification. Third, both perceived trust and perceived risk had a constructive impact on behavioral intentions within Gen Z’s demographic data. Based on these outcomes, food industry enterprises should proactively develop and bolster community experiential value, thereby encouraging streaming platform users to transform into brand consumers and advocates. Full article
Show Figures

Figure 1

17 pages, 1857 KiB  
Article
Breaking the Fifth Wall: Two Studies of the Effects of Observing Interpersonal Communication with Content Creators on YouTube
by Ezgi Ulusoy, Brandon Van Der Heide, Siyuan Ma, Kelsey Earle and Adam J. Mason
Behav. Sci. 2024, 14(2), 140; https://doi.org/10.3390/bs14020140 - 16 Feb 2024
Viewed by 2577
Abstract
Two studies were conducted to test the convergence of mass and interpersonal media processes and their effects on YouTube. The first study examined the influence of interpersonal interactions on video enjoyment. The results indicated that positive comment valence affected participants’ identification with the [...] Read more.
Two studies were conducted to test the convergence of mass and interpersonal media processes and their effects on YouTube. The first study examined the influence of interpersonal interactions on video enjoyment. The results indicated that positive comment valence affected participants’ identification with the content creator, which then affected enjoyment of the video. To investigate the effects of convergence from a macro-level perspective, the second study tracked and recorded data from 32 YouTube videos for 34 days and recorded the following data for each video: number of views, likes, and comments/responses. The results indicated that the more content creators and users interact, the more likes the video receives. However, user-to-user interactions are associated with a decrease in the number of likes a video receives. Full article
(This article belongs to the Special Issue Social Media as Interpersonal and Masspersonal)
Show Figures

Figure 1

25 pages, 8152 KiB  
Article
Automated Parts-Based Model for Recognizing Human–Object Interactions from Aerial Imagery with Fully Convolutional Network
by Yazeed Yasin Ghadi, Manahil Waheed, Tamara al Shloul, Suliman A. Alsuhibany, Ahmad Jalal and Jeongmin Park
Remote Sens. 2022, 14(6), 1492; https://doi.org/10.3390/rs14061492 - 19 Mar 2022
Cited by 23 | Viewed by 3132
Abstract
Advanced aerial images have led to the development of improved human–object interaction recognition (HOI) methods for usage in surveillance, security, and public monitoring systems. Despite the ever-increasing rate of research being conducted in the field of HOI, the existing challenges of occlusion, scale [...] Read more.
Advanced aerial images have led to the development of improved human–object interaction recognition (HOI) methods for usage in surveillance, security, and public monitoring systems. Despite the ever-increasing rate of research being conducted in the field of HOI, the existing challenges of occlusion, scale variation, fast motion, and illumination variation continue to attract more researchers. In particular, accurate identification of human body parts, the involved objects, and robust features is the key to effective HOI recognition systems. However, identifying different human body parts and extracting their features is a tedious and rather ineffective task. Based on the assumption that only a few body parts are usually involved in a particular interaction, this article proposes a novel parts-based model for recognizing complex human–object interactions in videos and images captured using ground and aerial cameras. Gamma correction and non-local means denoising techniques have been used for pre-processing the video frames and Felzenszwalb’s algorithm has been utilized for image segmentation. After segmentation, twelve human body parts have been detected and five of them have been shortlisted based on their involvement in the interactions. Four kinds of features have been extracted and concatenated into a large feature vector, which has been optimized using the t-distributed stochastic neighbor embedding (t-SNE) technique. Finally, the interactions have been classified using a fully convolutional network (FCN). The proposed system has been validated on the ground and aerial videos of the VIRAT Video, YouTube Aerial, and SYSU 3D HOI datasets, achieving average accuracies of 82.55%, 86.63%, and 91.68% on these datasets, respectively. Full article
(This article belongs to the Section AI Remote Sensing)
Show Figures

Figure 1

16 pages, 3627 KiB  
Article
SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic
by Muhammad U. S. Khan, Syed M. A. H. Bukhari, Tahir Maqsood, Muhammad A. B. Fayyaz, Darren Dancey and Raheel Nawaz
Electronics 2022, 11(3), 350; https://doi.org/10.3390/electronics11030350 - 24 Jan 2022
Cited by 15 | Viewed by 5034
Abstract
Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos [...] Read more.
Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos from someone who can sniff the network traffic. The present work explores the methodologies and features to identify the videos in a VPN and non-VPN network traffic. To identify such videos, a side-channel attack using a Sequential Convolution Neural Network is proposed. The results demonstrate that a sequence of bytes per second from even one-minute sniffing of network traffic is sufficient to predict the video with high accuracy. The accuracy is increased to 90% accuracy in the non-VPN, 66% accuracy in the VPN, and 77% in the mixed VPN and non-VPN traffic, for models with two-minute sniffing. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)
Show Figures

Figure 1

20 pages, 33352 KiB  
Article
CNN-Based Multi-Modal Camera Model Identification on Video Sequences
by Davide Dal Cortivo, Sara Mandelli, Paolo Bestagini and Stefano Tubaro
J. Imaging 2021, 7(8), 135; https://doi.org/10.3390/jimaging7080135 - 5 Aug 2021
Cited by 18 | Viewed by 4137
Abstract
Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on [...] Read more.
Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on the problem of camera model identification for video sequences, that is, given a video under analysis, detecting the camera model used for its acquisition. To this purpose, we develop two different CNN-based camera model identification methods, working in a novel multi-modal scenario. Differently from mono-modal methods, which use only the visual or audio information from the investigated video to tackle the identification task, the proposed multi-modal methods jointly exploit audio and visual information. We test our proposed methodologies on the well-known Vision dataset, which collects almost 2000 video sequences belonging to different devices. Experiments are performed, considering native videos directly acquired by their acquisition devices and videos uploaded on social media platforms, such as YouTube and WhatsApp. The achieved results show that the proposed multi-modal approaches significantly outperform their mono-modal counterparts, representing a valuable strategy for the tackled problem and opening future research to even more challenging scenarios. Full article
(This article belongs to the Special Issue Image and Video Forensics)
Show Figures

Figure 1

15 pages, 3712 KiB  
Article
Spotting Deepfakes and Face Manipulations by Fusing Features from Multi-Stream CNNs Models
by Semih Yavuzkilic, Abdulkadir Sengur, Zahid Akhtar and Kamran Siddique
Symmetry 2021, 13(8), 1352; https://doi.org/10.3390/sym13081352 - 26 Jul 2021
Cited by 14 | Viewed by 4553
Abstract
Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of another person’s face using artificial neural networks. Deepfake manipulations may be done [...] Read more.
Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of another person’s face using artificial neural networks. Deepfake manipulations may be done with a variety of techniques and applications. A quintessential countermeasure against deepfake or face manipulation is deepfake detection method. Most of the existing detection methods perform well under symmetric data distributions, but are still not robust to asymmetric datasets variations and novel deepfake/manipulation types. In this paper, for the identification of fake faces in videos, a new multistream deep learning algorithm is developed, where three streams are merged at the feature level using the fusion layer. After the fusion layer, the fully connected, Softmax, and classification layers are used to classify the data. The pre-trained VGG16 model is adopted for transferred CNN1stream. In transfer learning, the weights of the pre-trained CNN model are further used for training the new classification problem. In the second stream (transferred CNN2), the pre-trained VGG19 model is used. Whereas, in the third stream, the pre-trained ResNet18 model is considered. In this paper, a new large-scale dataset (i.e., World Politicians Deepfake Dataset (WPDD)) is introduced to improve deepfake detection systems. The dataset was created by downloading videos of 20 different politicians from YouTube. Over 320,000 frames were retrieved after dividing the downloaded movie into little sections and extracting the frames. Finally, various manipulations were performed to these frames, resulting in seven separate manipulation classes for men and women. In the experiments, three fake face detection scenarios are investigated. First, fake and real face discrimination is studied. Second, seven face manipulations are performed, including age, beard, face swap, glasses, hair color, hairstyle, smiling, and genuine face discrimination. Third, performance of deepfake detection system under novel type of face manipulation is analyzed. The proposed strategy outperforms the prior existing methods. The calculated performance metrics are over 99%. Full article
Show Figures

Figure 1

Back to TopTop