Wet Paper Coding-Based Deep Neural Network Watermarking
In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add wat...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/9/3489 |
| _version_ | 1797502772726202368 |
|---|---|
| author | Xuan Wang Yuliang Lu Xuehu Yan Long Yu |
| author_facet | Xuan Wang Yuliang Lu Xuehu Yan Long Yu |
| author_sort | Xuan Wang |
| collection | DOAJ |
| description | In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering. |
| first_indexed | 2024-03-10T03:40:53Z |
| format | Article |
| id | doaj.art-49791ae7ea114e84b37e1c5c77f68833 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:40:53Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-49791ae7ea114e84b37e1c5c77f688332023-11-23T09:19:14ZengMDPI AGSensors1424-82202022-05-01229348910.3390/s22093489Wet Paper Coding-Based Deep Neural Network WatermarkingXuan Wang0Yuliang Lu1Xuehu Yan2Long Yu3College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaIn recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering.https://www.mdpi.com/1424-8220/22/9/3489deep neural networkwatermarkingwet paper encodingembedding rate |
| spellingShingle | Xuan Wang Yuliang Lu Xuehu Yan Long Yu Wet Paper Coding-Based Deep Neural Network Watermarking Sensors deep neural network watermarking wet paper encoding embedding rate |
| title | Wet Paper Coding-Based Deep Neural Network Watermarking |
| title_full | Wet Paper Coding-Based Deep Neural Network Watermarking |
| title_fullStr | Wet Paper Coding-Based Deep Neural Network Watermarking |
| title_full_unstemmed | Wet Paper Coding-Based Deep Neural Network Watermarking |
| title_short | Wet Paper Coding-Based Deep Neural Network Watermarking |
| title_sort | wet paper coding based deep neural network watermarking |
| topic | deep neural network watermarking wet paper encoding embedding rate |
| url | https://www.mdpi.com/1424-8220/22/9/3489 |
| work_keys_str_mv | AT xuanwang wetpapercodingbaseddeepneuralnetworkwatermarking AT yulianglu wetpapercodingbaseddeepneuralnetworkwatermarking AT xuehuyan wetpapercodingbaseddeepneuralnetworkwatermarking AT longyu wetpapercodingbaseddeepneuralnetworkwatermarking |