MuST: robust image watermarking for multi-source tracing
AUTHORs: 
Guanjie Wang, Zehua Ma, Chang Liu, Xi Yang, Han Fang, Weiming Zhang, Nenghai YuAuthors Info & Claims
Guanjie Wang, Zehua Ma, Chang Liu, Xi Yang, Han Fang, Weiming Zhang, Nenghai YuAuthors Info & ClaimsArticle No.: 596, Pages 5364 - 5371
Abstract
In recent years, with the popularity of social media applications, massive digital images are available online, which brings great convenience to image recreation. However, the use of unauthorized image materials in multi-source composite images is still inadequately regulated, which may cause significant loss and discouragement to the copyright owners of the source image materials. Ideally, deep watermarking techniques could provide a solution for protecting these copyrights based on their encoder-noise-decoder training strategy. Yet existing image watermarking schemes, which are mostly designed for single images, cannot well address the copyright protection requirements in this scenario, since the multi-source image composing process commonly includes distortions that are not well investigated in previous methods, e.g., the extreme downsizing.
To meet such demands, we propose MuST, a multi-source tracing robust watermarking scheme, whose architecture includes a multi-source image detector and minimum external rectangle operation for multiple watermark resynchronization and extraction. Furthermore, we constructed an image material dataset covering common image categories and designed the simulation model of the multi-source image composing process as the noise layer. Experiments demonstrate the excellent performance of MuST in tracing sources of image materials from the composite images compared with SOTA watermarking methods, which could maintain the extraction accuracy above 98% to trace the sources of at least 3 different image materials while keeping the average PSNR of watermarked image materials higher than 42.51 dB. We released our code on https://github.com/MrCrims/MuST.
References
[1]
Almohammad, A.; and Ghinea, G. 2010. Stego image quality and the reliability of PSNR. In 2010 2nd International Conference on Image Processing Theory, Tools and Applications, 215-220. IEEE.
[2]
Bai, Y.; Chen, Y.; Yu, W.; Wang, L.; and Zhang, W. 2020. Products-10k: A large-scale product recognition dataset. arXiv preprint arXiv:2008.10545.
[3]
Dong, J.; Wang, W.; and Tan, T. 2013. CASIA Image Tampering Detection Evaluation Database. In 2013 IEEE China Summit and International Conference on Signal and Information Processing.
[4]
Downs, L.; Francis, A.; Koenig, N.; Kinman, B.; Hickman, R.; Reymann, K.; McHugh, T. B.; and Vanhoucke, V. 2022. Google scanned objects: A high-quality dataset of 3d scanned household items. In 2022 International Conference on Robotics and Automation (ICRA), 2553-2560. IEEE.
[5]
E. Riba, D. P. E. R., D. Mishkin; and Bradski, G. 2020. Kornia: an Open Source Differentiable Computer Vision Library for PyTorch. In Winter Conference on Applications of Computer Vision.
[6]
Fernandez, P.; Sablayrolles, A.; Furon, T.; Jégou, H.; and Douze, M. 2022. Watermarking images in self-supervised latent spaces. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3054-3058. IEEE.
[7]
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2020. Generative adversarial networks. Communications of the ACM, 139-144.
[8]
Gould, S.; Fulton, R.; and Koller, D. 2009. Decomposing a scene into geometric and semantically consistent regions. In 2009 IEEE 12th international conference on computer vision, 1-8. IEEE.
[9]
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
[10]
Hu, J.; Shen, L.; and Sun, G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132-7141.
[11]
Jia, J.; Gao, Z.; Zhu, D.; Min, X.; Zhai, G.; and Yang, X. 2022. Learning invisible markers for hidden codes in offline-to-online photography. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2273-2282.
[12]
Jia, Z.; Fang, H.; and Zhang, W. 2021. Mbrs: Enhancing robustness of dnn-based watermarking by mini-batch of real and simulated jpeg compression. In Proceedings of the 29th ACM international conference on multimedia, 41-49.
[13]
Klasson, M.; Zhang, C.; and Kjellstrom, H. 2019. A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels. In IEEE Winter Conference on Applications of Computer Vision (WACV).
[14]
Kroll, T. 2023. A Single Picture Database Is Worth a Thousand Statutory Damages Awards.
[15]
Li, Y. 2019. Vision China Copyright Dispute.
[16]
Loshchilov, I.; and Hutter, F. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.
[17]
Luo, Y.; Zhou, T.; Cui, S.; Ye, Y.; Liu, F.; and Cai, Z. 2023. Fixing the Double Agent Vulnerability of Deep Watermarking: A Patch-Level Solution against Artwork Plagiarism. IEEE Transactions on Circuits and Systems for Video Technology.
[18]
Ma, R.; Guo, M.; Hou, Y.; Yang, F.; Li, Y.; Jia, H.; and Xie, X. 2022. Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms. In Proceedings of the 30th ACM International Conference on Multimedia, 1532-1542.
[19]
Ma, Z.; Zhang, W.; Fang, H.; Dong, X.; Geng, L.; and Yu, N. 2021. Local geometric distortions resilient watermarking scheme based on symmetry. IEEE Transactions on Circuits and Systems for Video Technology, 31(12): 4826-4839.
[20]
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
[21]
Pham, N. T.; Lee, J.-W.; Kwon, G.-R.; and Park, C.-S. 2019. Hybrid Image-Retrieval Method for Image-Splicing Validation. Symmetry, 83.
[22]
Ronneberger, O.; Fischer, P.; and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 234-241. Springer.
[23]
Tancik, M.; Mildenhall, B.; and Ng, R. 2020. Stegastamp: Invisible hyperlinks in physical photographs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2117-2126.
[24]
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4): 600-612.
[25]
Zhu, J.; Kaplan, R.; Johnson, J.; and Fei-Fei, L. 2018. HiD-DeN: Hiding Data With Deep Networks. In Proceedings of the 15th European Conference on Computer Vision, 682-697.
Index Terms
- MuST: robust image watermarking for multi-source tracing
Index terms have been assigned to the content through auto-classification.
Recommendations
A region-adaptive semi-fragile dual watermarking scheme
Since existing watermarking schemes usually have only a single function, a region-adaptive semi-fragile dual watermarking scheme is proposed, taking into account both watermark embedding capacity and security. The dual watermarks refer to the robust ...
Visible watermarking with reversibility of multimedia images for ownership declarations
Digital watermarking technology is primarily the joining of the rightful owner of the protected media. Once the media are suspected to be illegally used, an open algorithm can be used to extract the digital watermark for the purpose of showing the media'...
A steganographic method based upon JPEG and quantization table modification
Special issue: Intelligent multimedia computing and networkingIn this paper, a novel steganographic method based on joint photographic expert-group (JPEG) is proposed. The proposed method modifies the quantization table first. Next, the secret message is hidden in the cover-image with its middle-frequency of the ...
Comments
Information & Contributors
Information
Published In
February 2024
23861 pages
ISBN:978-1-57735-887-9
Copyright © 2024 Association for the Advancement of Artificial Intelligence.
Sponsors
- Association for the Advancement of Artificial Intelligence
Publisher
AAAI Press
Publication History
Published: 07 January 2025
Qualifiers
- Research-article
- Research
- Refereed limited
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Jan 2025