Robust Video Hashing with Non-negative Tensor Factorization for Copy Detection
Authors: 
Mengzhu Yu, Zhenjun Tang, Huijiang Zhuang, Xiaoping Liang, Zhixin Li, Xianquan ZhangAuthors Info & Claims
Mengzhu Yu, Zhenjun Tang, Huijiang Zhuang, Xiaoping Liang, Zhixin Li, Xianquan ZhangAuthors Info & ClaimsPages 1094 - 1098
Abstract
Copy detection is a key task of video copyright protection. This paper presents a robust video hashing with non-negative tensor factorization (NTF) for copy detection. In the presented video hashing scheme, secondary frames are computed from the preprocessed video by assigning weights to all frames within a video group based on color entropy. Next, the secondary frames are fed into the pre-trained MobileNetV2 and then NTF is exploited to compress the three-order tensor constructed by stacking the output feature maps for hash construction. Experiments conducted on publicly available video datasets indicate that the presented hashing scheme outperforms the evaluated hashing schemes in the performances of classification and copy detection.
References
[1]
Lv Chen, Dengpan Ye, Yueyun Shang, and Jiaqing Huang. Robust video hashing based on local fluctuation preserving for tracking deep fake videos. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2894--2898, Singapore, 2022.
[2]
Gaobo Yang, Ning Chen, and Qin Jiang. A robust hashing algorithm based on SURF for video copy detection. Computers & Security, 31(1):33--39, 2012.
[3]
Lv Chen, Dengpan Ye, and Yueyun Shang. Perceptual video hashing with secure anti-noise model for social video retrieval. IEEE Internet of Things Journal, 11(2):2648--2664, 2024.
[4]
Zhenjun Tang, Shaopeng Zhang, Xian Quan Zhang, Zhixin Li, Zhenhai Chen, and Chunqiang Yu. Video hashing with secondary frames and invariant moments. Journal of Visual Communication and Image Representation, 79, Article ID. 103209, 2021.
[5]
Mani Malek Esmaeili, Mehrdad Fatourechi, and Rabab Kreidieh Ward. A robust and fast video copy detection system using content-based fingerprinting. IEEE Transactions on Information Forensics and Security, 6(1):213--226, 2011.
[6]
Sandeep Rameshnath and P. K. Bora. Perceptual video hashing based on temporal wavelet transform and random projections with application to indexing and retrieval of near-identical videos. Multimedia Tools and Applications, 78:18055--18075, 2019.
[7]
Fouad Khelifi and Ahmed Bouridane. Perceptual video hashing for content identification and authentication. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):50--67, 2019.
[8]
Neelima Arambam and Kh. M SinghKh. Collusion and rotation resilient video hashing based on scale invariant feature transform. The Imaging Science Journal, 65(1):62--74, 2017.
[9]
Mu Li and Vishal Monga. Twofold video hashing with automatic synchronization. IEEE Transactions on Information Forensics and Security, 10(8):1727--1738, 2015.
[10]
Yassine Himeur and Karima Ait Sadi. Joint color and texture descriptor using ring decomposition for robust video copy detection in large databases. In Proceedings of IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pages 495--500, Abu Dhabi, United Arab Emirates, 2015.
[11]
Xiushan Nie, Yane Chai, Ju Liu, Jiande Sun, and Yilong Yin. Spherical torus-based video hashing for near-duplicate video detection. Science China Information Sciences, 59:235--237, 2016.
[12]
Zhenhai Chen, Zhenjun Tang, Xinpeng Zhang, Ronghai Sun, and Xianqun Zhang. Efficient video hashing based on low-rank frames. IET Image Processing, 16:344--355, 2022.
[13]
Lv Chen, Dengpan Ye, and Shunzhi Jiang. High accuracy perceptual video hashing via low-rank decomposition and DWT. In Proceedings of International Conference on Multimedia Modeling (MMM), pages 802--812, Daejeon, South Korea, 2020.
[14]
Thierry Pun. Entropic thresholding, a new approach. Computer Graphics and Image Processing, 16(3):210--239, 1981.
[15]
A Ardeshir Goshtasby. High dynamic range reduction via maximization of image information. Preprint of CSE Department, Wright State University, [Online]. Available: http://www.cs.wright.edu/~aghoshtas/hdr.html, 2003.
[16]
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4510--4520, Salt Lake City, UT, USA, 2018.
[17]
Barbara Krausz and Christian Bauckhage. Action recognition in videos using nonnegative tensor factorization. In Proceedings of 20th International Conference on Pattern Recognition, pages 1763--1766, Istanbul, Turkey, 2010.
[18]
Çağda§ Bilen, Alexey Ozerov, and Patrick Pérez. Automatic allocation of ntf components for user-guided audio source separation. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 484--488, Shanghai, China, 2016.
[19]
Liguo Yin, Zhengyao Bai, and Renqing Yang. Video forgery detection based on nonnegative tensor factorization. In Proceedings of 4th IEEE International Conference on Information Science and Technology, pages 148--151, Shenzhen, China, 2014.
[20]
Amnon Shashua and Tamir Hazan. Non-negative tensor factorization with applications to statistics and computer vision. In Proceedings of the 22nd International Conference on Machine Learning, pages 792--799, New York, NY, USA, 2005.
[21]
Stefanos Zafeiriou. Discriminant nonnegative tensor factorization algorithms. IEEE Transactions on Neural Networks, 20(2):217--235, 2009.
[22]
Guoxu Zhou, Andrzej Cichocki, and Shengli Xie. Fast nonnegative matrix/tensor factorization based on low-rank approximation. IEEE Transactions on Signal Processing, 60(6):2928--2940, 2012.
[23]
Reefvid: Free reef video clip database, [Online]. available: http://www.reefvid.org/, accessed January 18, 2023.
[24]
Tom Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27(8):861--874, 2006.
[25]
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. HMDB: A large video database for human motion recognition. In Proceedings of the International Conference on Computer Vision (ICCV), pages 2556--2563, Barcelona, Spain, 2011.
Index Terms
- Robust Video Hashing with Non-negative Tensor Factorization for Copy Detection
Recommendations
Robust Image Hashing via CP Decomposition and DCT for Copy Detection
Copy detection is a key task of image copyright protection. This article proposes a robust image hashing algorithm by CP decomposition and discrete cosine transform (DCT) for copy detection. The first contribution is the third-order tensor construction ...
Computing non-negative tensor factorizations
Mathematical programming in data mining and machine learningNon-negative tensor factorization (NTF) is a technique for computing a parts-based representation of high-dimensional data. NTF excels at exposing latent structures in datasets, and at finding good low-rank approximations to the data. We describe an ...
Non-Negative Tensor Factorization Applied to Music Genre Classification
Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the ...
Comments
Information & Contributors
Information
Published In
May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
- General Chairs:
- Cathal Gurrin,
- Rachada Kongkachandra,
- Klaus Schoeffmann,
- Program Chairs:
- Duc-Tien Dang-Nguyen,
- Luca Rossetto,
- Shin'ichi Satoh,
- Liting Zhou
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 June 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
- Guangxi ?Bagui Scholar? Team for Innovation and Research
- Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing
- National Natural Science Foundation of China
- Guangxi Talent Highland Project of Big Data Intelligence and Application
- Guangxi Natural Science Foundation
Conference
Acceptance Rates
Overall Acceptance Rate 254 of 830 submissions, 31%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 58Total Downloads
- Downloads (Last 12 months)58
- Downloads (Last 6 weeks)7
Reflects downloads up to 04 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in