Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN
Abstract The image protection of non‐fungible tokens based on zero‐watermark methods has received widespread attention. However, on the one hand, existing zero‐watermark methods are often limited to complex texture changes in the host image, and the features for constructing the zero‐watermark are v...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.13291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846121441537294336 |
|---|---|
| author | Yiran Peng Chenheng Deng Jiaqi Li KinTak U |
| author_facet | Yiran Peng Chenheng Deng Jiaqi Li KinTak U |
| author_sort | Yiran Peng |
| collection | DOAJ |
| description | Abstract The image protection of non‐fungible tokens based on zero‐watermark methods has received widespread attention. However, on the one hand, existing zero‐watermark methods are often limited to complex texture changes in the host image, and the features for constructing the zero‐watermark are vulnerable to geometric attacks. On the other hand, a single watermark image cannot adapt to the diverse usage scenarios of non‐fungible tokens. This paper proposes a robust personalized zero‐watermark scheme to address the challenges above. Firstly, the image regions suitable for constructing zero‐watermarks are highlighted by the non‐uniform weighted reconstruction, and the U‐Net is introduced for feature extraction against the geometric attacks. At the same time, the watermark images generated by generative models that are suitable for the usage scenario have achieved zero‐watermark addition and protection for non‐fungible token image content. The proposed method has been experimentally verified to have a certain degree of robustness in geometric and non‐geometric attacks. |
| format | Article |
| id | doaj-art-5d7accf5a2b14b889fde3afe79e1ed38 |
| institution | Kabale University |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-5d7accf5a2b14b889fde3afe79e1ed382024-12-16T04:00:31ZengWileyIET Image Processing1751-96591751-96672024-12-0118144862487210.1049/ipr2.13291Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGANYiran Peng0Chenheng Deng1Jiaqi Li2KinTak U3School of Computer Science and Engineering, Faculty of Innovation Engineering Macau University of Science and Technology Macau ChinaSchool of Computer Science and Engineering, Faculty of Innovation Engineering Macau University of Science and Technology Macau ChinaFaculty of Humanities and Arts Macau University of Science and Technology Macau ChinaSchool of Computer Science and Engineering, Faculty of Innovation Engineering Macau University of Science and Technology Macau ChinaAbstract The image protection of non‐fungible tokens based on zero‐watermark methods has received widespread attention. However, on the one hand, existing zero‐watermark methods are often limited to complex texture changes in the host image, and the features for constructing the zero‐watermark are vulnerable to geometric attacks. On the other hand, a single watermark image cannot adapt to the diverse usage scenarios of non‐fungible tokens. This paper proposes a robust personalized zero‐watermark scheme to address the challenges above. Firstly, the image regions suitable for constructing zero‐watermarks are highlighted by the non‐uniform weighted reconstruction, and the U‐Net is introduced for feature extraction against the geometric attacks. At the same time, the watermark images generated by generative models that are suitable for the usage scenario have achieved zero‐watermark addition and protection for non‐fungible token image content. The proposed method has been experimentally verified to have a certain degree of robustness in geometric and non‐geometric attacks.https://doi.org/10.1049/ipr2.13291image watermarkingwatermarking |
| spellingShingle | Yiran Peng Chenheng Deng Jiaqi Li KinTak U Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN IET Image Processing image watermarking watermarking |
| title | Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN |
| title_full | Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN |
| title_fullStr | Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN |
| title_full_unstemmed | Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN |
| title_short | Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN |
| title_sort | personalized zero watermark algorithm based on non uniform weighted reconstruction and wgan |
| topic | image watermarking watermarking |
| url | https://doi.org/10.1049/ipr2.13291 |
| work_keys_str_mv | AT yiranpeng personalizedzerowatermarkalgorithmbasedonnonuniformweightedreconstructionandwgan AT chenhengdeng personalizedzerowatermarkalgorithmbasedonnonuniformweightedreconstructionandwgan AT jiaqili personalizedzerowatermarkalgorithmbasedonnonuniformweightedreconstructionandwgan AT kintaku personalizedzerowatermarkalgorithmbasedonnonuniformweightedreconstructionandwgan |