VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval

2025-07-18

AI Summary

Visualization dissemination relies on raster images, resulting in the loss of source code, interactivity, and metadata. Existing embedded visualization image data retrieval (VIDR) methods exhibit insufficient robustness against common manipulations such as cropping and editing. To address this, we propose a tamper-resilient VIDR framework that innovatively integrates repetitive data tiling, reversible information broadcasting, and anchor-driven cropping localization. Our approach synergistically combines digital watermarking, redundant encoding, and image anchor matching to enable high-capacity, high-security embedding and recovery of metadata links. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in retrieval accuracy, embedding capacity, tamper robustness—including against cropping, geometric distortion, and post-processing—and resistance to steganalysis attacks. Furthermore, it supports interactive chart reconstruction, precise tamper localization, and copyright protection.

Technology Category

• Computer Vision: Image and Video Retrieval
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• Securing visualization images against tampering during online distribution
• Recovering embedded metadata links after image editing or cropping
• Enhancing robustness of Visualization Image Data Retrieval (VIDR) methods

Innovation

• Tamper-resistant metadata embedding in images
• Robust techniques for data recovery after tampering
• Anchor-based scheme for crop localization