The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Embedding Watermarks into Deep Neural Networks
Open accessAbstract
Significant progress has been made with deep neural networks recently. Sharing trained models of deep neural networks has been a very important in the rapid progress of research and development of these systems. At the same time, it is necessary to protect the rights to shared trained models. To this end, we propose to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models. First, we formulate a new problem: embedding watermarks into deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.
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Going deeper with convolutions
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
Distilling the Knowledge in a Neural Network
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Caffe: Convolutional Architecture for Fast Feature Embedding
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Adversarial frontier stitching for remote neural network watermarking
Le Merrer, E., Pérez, P., & Trédan, G. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04434-z · Cited by 341
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362
Privacy and Security Issues in Deep Learning: A Survey
Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2021) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 282
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02053 · Cited by 251
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01418 · Cited by 195
Metadata
- Authors
- Uchida, Y., Nagai, Y., Sakazawa, S., & Satoh, S.
- Venue
- Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval
- Publisher
- ACM
- Published
- 2017-06-06
- Pages
- 269-277
- OpenAlex
- W2579318729
- Cited by
- 676
- References
- 31
- FWCI
- 7.12
- Type
- conference paper
- Language
- en
- Open access
- green
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- Uchida, Y., Nagai, Y., Sakazawa, S., & Satoh, S. (2017). Embedding Watermarks into Deep Neural Networks. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. https://doi.org/10.1145/3078971.3078974
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