VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning

Data visualizations are widely used for communicating information, but they are also vulnerable to intentional manipulations that induce misleading interpretations. Existing methods focus on locating tampered regions or recovering hidden information, without explaining how the visualization has been manipulated or why the resulting changes may mislead viewers. We propose **VizAnchor**, a framework for visualization manipulation understanding through dual-anchor evidence construction and VLM-based reasoning. In the first stage, VizAnchor constructs a semantic anchor to recover authentic chart information and a spatial anchor to localize tampered regions. In the second stage, three task-specific VLM reasoning steps progressively ground manipulation attributes, reconstruct original and tampered chart narratives, and infer misleading intent by integrating visual evidence with intermediate reasoning results. We further construct a dataset for tampering localization and a dataset for misleading intent inferring. Evaluation shows that VizAnchor accurately localizes manipulations and produces faithful explanations of their manipulation, misleaders, and misleading intents.