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

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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 \textbf{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 ch

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