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arXiv · 2512.18853

VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference

Sicheng Song·Yanjie Zhang·Zixin Chen·Huamin Qu·Changbo Wang·Chenhui Li
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Abstract

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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Sicheng Song, Yanjie Zhang, Zixin Chen, Huamin Qu, Changbo Wang, Chenhui Li. 2025-12-21. VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference. https://arxiv.org/abs/2512.18853

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