№1, 2026
Digital watermarking has emerged as a critical technology for safeguarding intellectual property rights, particularly in an era where digital content generation is accelerating exponentially. In this study, we present a comprehensive evaluation and comparative analysis of three prominent deep learning-based models—namely a simple Convolutional Neural Network (CNN), ResNet-50, and EfficientNet-B0—for the task of invisible watermark detection. These models are tested using images embedded with a binary logo watermark through a CNN-based encoder-decoder system. Our experimentation leverages 2,000 labeled images from the MS COCO dataset, evenly split between clean and watermarked classes. We conduct thorough evaluations based on several key metrics, including detection accuracy, precision, recall, F1-score, area under the ROC curve (AUC), model storage size, and inference latency. Our results show that all models demonstrate strong detection capabilities, with ResNet-50 and EfficientNet-B0 reaching near-perfect performance. Notably, EfficientNet-B0 offers an optimal balance of performance and efficiency, making it ideal for real-time watermark verification in practical deployment scenarios (pp.44-49).
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, doi:10.1145/3065386
- Azer Kerimov (2024). An algorithm of the sequence of artificial symmetric signals for comparing and creating a new convolution method. Problems of Information Society, 15(2), 24-29.
- Haribabu Kandi, Deepak Mishra, and Subrahmanyam RK Sai Gorthi (2017). Exploring the learning capabilities of convolutional neural networks for robust image watermarking. Computers & Security, 65:247–268, https://doi.org/10.1016/j.cose.2016.11.016
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep learning. MIT press,
doi:10.1007/s10710-017-9314-z - Ingemar J. Cox, Matt L. Miller (2001). A Review of Watermarking and the Importance of Perceptual Modeling. Proceedings of SPIE - The International Society for Optical Engineering. doi:10.1117/12.274502
- Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei (2018). Hidden: Hiding data with deep networks. In Proceedings of the European conference on computer vision (ECCV), pp.657–672, https://doi.org/10.48550
- Junxiu Liu, Jiadong Huang, Yuling Luo, Lvchen Cao, Suyang, Duqu Wei, and Ronglong Zhou. An optimized image watermarking method based on HD and SVD in DWT domain, doi:10.1109/ACCESS.2019.2915596
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (2015). Deep Residual Learning for Image Recognition. Computer Vision and Pattern Recognition, 10 Dec 2015.
https://doi.org/10.48550 - Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni (2019). Robust Invisible Video Watermarking with Attention. Computer Vision and Pattern Recognition. https://doi.org/10.48550
- Mingxing Tan, Quoc V. Le (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. https://doi.org/10.48550
- Ramin Rzayev, Azer Kerimov, Uzeyir Gurbanli, Fuad Salmanov (2024). Criteria for assessing the adequacy of image recognition methods and their verification using examples of artificial series of signals. Problems of Information Society, 15(1), 10-17.
- Seung-Min Mun, Seung-Hun Nam, Han-Ul Jang, Dongkyu Kim, and Heung-Kyu Lee (2017). A robust blind watermarking using convolutional neural network. arXiv preprint arXiv:1704.03248, https://doi.org/10.1016/j.neucom.2019.01.067
- Vedran Vukoti´c, Vivien Chappelier, and Teddy Furon (2018). Are deep neural networks good for blind image watermarking? In 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7. IEEE, https://doi.org/10.3390/e22020198
- X. Zhong, P. -C. Huang, S. Mastorakis and F. Y. Shih (2021). "An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks," in IEEE Transactions on Multimedia, vol. 23, pp. 1951-1961,
doi: 10.1109/TMM.2020.3006415. - Yang Liu, Mengxi Guo, Jian Zhang, Yuesheng Zhu, and Xiaodong Xie (2019). A novel two-stage separable deep learning framework for practical blind watermarking. In Proceedings of the 27th ACM International Conference on Multimedia, pp. 1509–1517, doi: 10.1145/3343031.3351025
- Yann Lecun, Leon Bottou, Y. Bengio (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE 86(11):2278 – 2324, doi:10.1109/5.726791
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