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.

翻译：数据可视化的完整性正日益受到图像编辑技术的威胁，这些技术能够实现隐蔽且具有欺骗性的篡改。通过一项形成性研究，我们界定了这一挑战，并将篡改技术主要分为两类：数据操纵与视觉编码操纵。为此，我们提出了VizDefender，一个用于篡改检测与分析的系统框架。该框架整合了两个核心组件：1）一个半脆弱水印模块，通过向图像中嵌入位置图来保护可视化，能够在保持视觉质量的同时精确定位被篡改区域；2）一个意图分析模块，利用多模态大语言模型（MLLMs）来解读篡改操作，推断攻击者的意图及其可能导致的误导性效果。大量的评估与用户研究验证了我们方法的有效性。