The Experts below are selected from a list of 14397 Experts worldwide ranked by ideXlab platform

Dong Wang - One of the best experts on this subject based on the ideXlab platform.

  • streamguard a bayesian network approach to Copyright Infringement detection problem in large scale live video sharing systems
    International Conference on Big Data, 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

  • crowdsourcing based Copyright Infringement detection in live video streams
    Advances in Social Networks Analysis and Mining, 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • ASONAM - Crowdsourcing-based Copyright Infringement detection in live video streams
    2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • BigData - StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

Daniel Yue Zhang - One of the best experts on this subject based on the ideXlab platform.

  • streamguard a bayesian network approach to Copyright Infringement detection problem in large scale live video sharing systems
    International Conference on Big Data, 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

  • crowdsourcing based Copyright Infringement detection in live video streams
    Advances in Social Networks Analysis and Mining, 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • ASONAM - Crowdsourcing-based Copyright Infringement detection in live video streams
    2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • BigData - StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

Yang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • streamguard a bayesian network approach to Copyright Infringement detection problem in large scale live video sharing systems
    International Conference on Big Data, 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

  • crowdsourcing based Copyright Infringement detection in live video streams
    Advances in Social Networks Analysis and Mining, 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • ASONAM - Crowdsourcing-based Copyright Infringement detection in live video streams
    2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • BigData - StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Daniel Yue Zhang, Yang Zhang, Lixing Song, Dong Wang
    Abstract:

    Copyright Infringement detection is a critical problem in large-scale online video sharing systems: the Copyright-infringing videos must be correctly identified and removed from the system to protect the Copyright of the content owners. This paper focuses on a challenging problem of detecting Copyright Infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and Copyright-infringing ones may have very similar visual content and descriptions. We found current commercial Copyright detection systems did not address this problem well: a large amount of Copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based Copyright Infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the Copyright-infringing videos.

Jose Badilla - One of the best experts on this subject based on the ideXlab platform.

  • crowdsourcing based Copyright Infringement detection in live video streams
    Advances in Social Networks Analysis and Mining, 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • ASONAM - Crowdsourcing-based Copyright Infringement detection in live video streams
    2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

Herman Tong - One of the best experts on this subject based on the ideXlab platform.

  • crowdsourcing based Copyright Infringement detection in live video streams
    Advances in Social Networks Analysis and Mining, 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.

  • ASONAM - Crowdsourcing-based Copyright Infringement detection in live video streams
    2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
    Co-Authors: Daniel Yue Zhang, Herman Tong, Jose Badilla, Yang Zhang, Dong Wang
    Abstract:

    With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes Copyright has emerged as a new critical problem in online social media. In contrast to the traditional Copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial Copyright Infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of Copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the Copyright Infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original Copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the Copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and Copyright-infringing ones. In this paper, we develop a crowdsourcing-based Copyright Infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting Copyright-infringing live videos on YouTube.