ACM Multimedia 2023 Grand Challenge Report: Invisible Video Watermark
Authors: 
Jin Chen, Yi Yu, Shien Song, Xinying Wang, Jie Yang, Yifei Xue, Yizhen LaoAuthors Info & Claims
Jin Chen, Yi Yu, Shien Song, Xinying Wang, Jie Yang, Yifei Xue, Yizhen LaoAuthors Info & ClaimsPages 9630 - 9634
Abstract
MGTV recently organized a pioneering Invisible Video Watermark Challenge, inviting participants to create a framework capable of embedding invisible watermarks into videos and extracting them from watermarked content.
The invisible watermark serves as a discrete digital signature within the media data, imperceptible to the human eye. This technique safeguards the ownership and authenticity of multimedia content. While convolutional neural networks have demonstrated remarkable efficacy in image and video processing, the discourse on invisible watermarking remains limited. This challenge, therefore, presents an opportune moment to advance the field of invisible watermarking.
Furthermore, to support this endeavor, we curated the comprehensive MGTV_WM dataset, encompassing diverse video types. For further details, please refer to our official website (https://challenge.ai.mgtv.com/\#/track/18?locale=en).
References
[1]
Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara, Xinqiao Zhang1, and Javier Duarte. 2022. FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs. arXiv preprint arXiv:2209.12391 (2022).
[2]
Saurav Joshi, Jeetendra Pande, and B. K. Singh. 2018. Watermarking of audio signals using iris data for protecting intellectual property rights of multiple owners. International Journal of Information Technology 10 (2018), 559--566.
[3]
J. Lee, H. Kim, Y. Yoon, and H. Kim. 2011. DCT-based invisible watermarking for image authentication. IEEE Transactions on Consumer Electronics 57, 2 (2011), 539--545.
[4]
Jae-Eun Lee, Young-Ho Seo, and Dong-Wook Kim. 2020. Convolutional neural network-based digital image watermarking adaptive to the resolution of image and watermark.
[5]
Y. Liu, S. Tang, R. Liu, L. Zhang, and Z. Ma. 2018. Secure and robust digital image watermarking scheme using logistic and RSA encryption. 95--105.
[6]
Rachna Patel, Kalpesh Lad, Mukesh Patel, and Madhavi Desai. 2021. An efficient dct-sbpm based video steganography in compressed domain. International Journal of Information Technology 13 (2021), 1073--1078.
[7]
Priya Porwal, Ratnesh N. Chaturvedi, Tanvi Ghag, Nikita Poddar, and Ankita Tawde. 2014. Digital Video Watermarking Using Least Significant Bit (LSB) Technique. International Journal of Engineering Research & Technology (IJERT) 3, 1 (January 2014), 2491--2496.
[8]
Sanjana Sinha, Prajnat Bardhan, Swarnali Pramanick, Ankul Jagatramka, Dipak K. Kole, and Aruna Chakraborty. 2011. Digital Video Watermarking using Discrete Wavelet Transform and Principal Component Analysis. International Journal of Wisdom Based Computing 1, 2 (August 2011), 7--12.
[9]
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. 2019. Deep High-Resolution Representation Learning for Human Pose Estimation. In CVPR.
[10]
Alireza Tavakoli, Zahra Honjani, and Hedieh Sajedi. 2022. Convolutional Neural Network-Based Image Watermarking using Discrete Wavelet Transform. arXiv preprint arXiv:2210.06179 (2022).
[11]
Yuexin Xiang, Tiantian Li, Wei Ren, and Tianqing Zhu. 2022. Generating Image Adversarial Examples by Embedding Digital Watermarks. arXiv preprint arXiv:2009.05107 (2022).
[12]
Jin Xun and Jong-Weon Kim. 2018. Robust Digital Watermarking for High- definition Video using Steerable Pyramid Transform, Two Dimensional Fast Fourier Transform and Ensemble Position-based Error Correcting. KSII Transactions on Internet and Information Systems 12, 7 (July 2018), 3438--3454. https: //doi.org/10.3837/tiis.2018.07.024
[13]
Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Robust Invisible Video Watermarking with Attention. (2019). arXiv:1909.01285 [cs.MM]
[14]
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR.
[15]
Han Fang Zhaoyang Jia and Weiming Zhang. 2021. MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression. In arXiv:2108.08211.
Index Terms
- ACM Multimedia 2023 Grand Challenge Report: Invisible Video Watermark
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Information & Contributors
Information
Published In
October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
- General Chairs:
- Abdulmotaleb El Saddik,
- Tao Mei,
- Rita Cucchiara,
- Program Chairs:
- Marco Bertini,
- Diana Patricia Tobon Vallejo,
- Pradeep K. Atrey,
- M. Shamim Hossain
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Publication History
Published: 27 October 2023
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- Research-article
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MM '23
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MM '23: The 31st ACM International Conference on Multimedia
October 29 - November 3, 2023
Ottawa ON, Canada
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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
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