VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911331407364096 |
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| author | Song, Sicheng Zhang, Yanjie Chen, Zixin Qu, Huamin Wang, Changbo Li, Chenhui |
| author_facet | Song, Sicheng Zhang, Yanjie Chen, Zixin Qu, Huamin Wang, Changbo Li, Chenhui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference Song, Sicheng Zhang, Yanjie Chen, Zixin Qu, Huamin Wang, Changbo Li, Chenhui Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.18853 |