用可定位水印和意图分析，识别可视化图像的篡改痕迹。
VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference
- 嵌入位置映射水印，精准定位篡改区域且不影响画质。
- 利用多模态大模型解析篡改行为，推断攻击者意图。
- 适合关注数据可信性与可视化安全的研究者使用。
数据可视化的真实性正面临图像编辑技术带来的隐蔽篡改威胁。通过前期研究，我们定义了这一挑战，并将篡改手法分为两类：数据操纵和视觉编码操纵。为此，我们提出 VizDefender 框架，用于篡改检测与分析。该框架包含两个核心组件：1）半脆弱水印模块，在图像中嵌入位置地图，实现篡改区域的精确定位，同时保持视觉质量；2）意图分析模块，利用多模态大语言模型（MLLMs）解析篡改行为，推断攻击者的意图及误导效果。大量评估与用户研究表明，该方法有效可靠。
原文摘要 · Abstract (English)
The integrity of data visualizations is increasingly threatened by image editing techniques that enable subtle yet deceptive tampering. Through a formative study, we define this challenge and categorize tampering techniques into two primary types: data manipulation and visual encoding manipulation. To address this, we present VizDefender, a framework for tampering detection and analysis. The framework integrates two core components: 1) a semi-fragile watermark module that protects the visualization by embedding a location map to images, which allows for the precise localization of tampered regions while preserving visual quality, and 2) an intent analysis module that leverages Multimodal Large Language Models (MLLMs) to interpret manipulation, inferring the attacker's intent and misleading effects. Extensive evaluations and user studies demonstrate the effectiveness of our methods.
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