Fighting Against Piracy:An Approach to Detect Pirated Video Websites Enhanced by Third-party Services

Zhao, Li; Zhang, Shijun; Yin, Jiangyi; Du, Meijie; Zhang, Zhongyi; Liu, Qingyun

doi:10.1109/iscc55528.2022.9912777

Cited by 3 publications

(2 citation statements)

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“…Usually, the RNN includes the LSTM and GRU, used for text classification, as shown in [8], [19], [25]. Some studies on the existing works use a combination of CNN and RNN, including the LSTM or GRU for text classification, as shown in [11], [15], [21], [23], [26]. From this, it can be seen that the CNN is flexible to use for image and text-based web page classification.…”

Section: Resultsmentioning

confidence: 99%

“…Majority of the existing works use accuracy to report the performance of web page classification model as shown in [8], [9], [11]- [19], [21]- [23], [25], [28]. The other metric used by the existing works to report the performance is F-measure as shown in [10], [20], [24], [26], [27]. In terms of performance value, the existing works use image classification shows high performance compared to text classification.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Topic-Based Web Page Classification Using Deep Learning

Apandi,

Sallim,

Mohamed

et al. 2023

JOIV : Int. J. Inform. Visualization

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The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Automatic Topic-Based Web Page Classification Using Deep Learning

Apandi,

Sallim,

Mohamed

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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FED-AT-VIDEO Nets—A Federated Capsule – Self Gated Learning Architecture For The Multi-View Video Summarization Technique

Kandaswamy,

Balachandern

2024

SIViP

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FED-AT-VIDEO NETS - A Federated Capsule – Self Gated Learning Architecture for the Multi-View Video Summarization Technique.

KANDASWAMY,

BALACHANDER

2023

Preprint

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Video analytics using the huge amount of data from the surveillance networks has become a core function for multiple applications such as object detection, human activity recognition, health care diagnosis and so on. Due to its massive nature, achieving efficient video summarization has become a vital challenge for constructing video analytics architecture. Moreover, these video contains private information, and security against different intruders has also added fuel to the existing challenges. In recent years, a number of architectures have been proposed to achieve better and secured multi-view summarization (MVS) techniques that can aid in better video analytics. Unfortunately, existing architecture needs brighter light of research to eradicate the aforementioned challenges. In this article, federated deep gated attention architecture (FDGAA) is proposed for attaining the secured MVS by organizing the computing and networking resources of cloud and edge cameras collectively. The proposed architecture is modeled as a three-tier framework which is precisely given as 1) Video collection unit (VDU) that collects the videos from the different views of the camera installed. 2) Distributed Training network(DTN) which consists of federated learning self-attention saliency Gated recurrent units(SAS-GRU) in which the training is collaboratively shared among the edges without sacrificing the privacy of video information. 3) Finally the extracted deep features are summarized in the cloud for further processing. Utilizing a variety of datasets and NVIDIA Nano Boards as edge nodes, substantial research is conducted to develop the Google Federated Tensorflow Libraries-based federated learning architecture. Performance has been compared with other MVS systems that are currently based on deep learning to demonstrate the superiority of the proposed framework. In comparison to other state-of-the-art MVS approaches, the experimental evaluation shows that the suggested model performs better.

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Fighting Against Piracy:An Approach to Detect Pirated Video Websites Enhanced by Third-party Services

Cited by 3 publications

References 0 publications

Automatic Topic-Based Web Page Classification Using Deep Learning

Automatic Topic-Based Web Page Classification Using Deep Learning

FED-AT-VIDEO Nets—A Federated Capsule – Self Gated Learning Architecture For The Multi-View Video Summarization Technique

FED-AT-VIDEO NETS - A Federated Capsule – Self Gated Learning Architecture for the Multi-View Video Summarization Technique.

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