Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14459 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913949608312832 |
|---|---|
| author | Ye, Huayuan Chen, Juntong Zhang, Shenzhuo Zhang, Yipeng Wang, Changbo Li, Chenhui |
| author_facet | Ye, Huayuan Chen, Juntong Zhang, Shenzhuo Zhang, Yipeng Wang, Changbo Li, Chenhui |
| contents | The dissemination of visualizations is primarily in the form of raster images, which often results in the loss of critical information such as source code, interactive features, and metadata. While previous methods have proposed embedding metadata into images to facilitate Visualization Image Data Retrieval (VIDR), most existing methods lack practicability since they are fragile to common image tampering during online distribution such as cropping and editing. To address this issue, we propose VisGuard, a tamper-resistant VIDR framework that reliably embeds metadata link into visualization images. The embedded data link remains recoverable even after substantial tampering upon images. We propose several techniques to enhance robustness, including repetitive data tiling, invertible information broadcasting, and an anchor-based scheme for crop localization. VisGuard enables various applications, including interactive chart reconstruction, tampering detection, and copyright protection. We conduct comprehensive experiments on VisGuard's superior performance in data retrieval accuracy, embedding capacity, and security against tampering and steganalysis, demonstrating VisGuard's competence in facilitating and safeguarding visualization dissemination and information conveyance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval Ye, Huayuan Chen, Juntong Zhang, Shenzhuo Zhang, Yipeng Wang, Changbo Li, Chenhui Computer Vision and Pattern Recognition The dissemination of visualizations is primarily in the form of raster images, which often results in the loss of critical information such as source code, interactive features, and metadata. While previous methods have proposed embedding metadata into images to facilitate Visualization Image Data Retrieval (VIDR), most existing methods lack practicability since they are fragile to common image tampering during online distribution such as cropping and editing. To address this issue, we propose VisGuard, a tamper-resistant VIDR framework that reliably embeds metadata link into visualization images. The embedded data link remains recoverable even after substantial tampering upon images. We propose several techniques to enhance robustness, including repetitive data tiling, invertible information broadcasting, and an anchor-based scheme for crop localization. VisGuard enables various applications, including interactive chart reconstruction, tampering detection, and copyright protection. We conduct comprehensive experiments on VisGuard's superior performance in data retrieval accuracy, embedding capacity, and security against tampering and steganalysis, demonstrating VisGuard's competence in facilitating and safeguarding visualization dissemination and information conveyance. |
| title | VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.14459 |