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  3. Vizdefender: Unmasking Visualization Tampering Through Proactive Localization And Intent Inference.
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  3. Vizdefender: Unmasking Visualization Tampering Through Proactive Localization And Intent Inference.

Related Experiment Videos

VizDefender: Unmasking Visualization Tampering Through Proactive Localization and Intent Inference.

Sicheng Song, Yanjie Zhang, Zixin Chen

    IEEE Transactions on Visualization and Computer Graphics
    |June 2, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    VizDefender is a new framework to detect and analyze tampering in data visualizations. It uses watermarking for precise localization and Multimodal Large Language Models (MLLMs) to understand manipulation intent.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Data Science
    • Information Security

    Background:

    • Data visualization integrity is crucial but threatened by sophisticated image editing.
    • Tampering techniques include data manipulation and visual encoding manipulation.
    • Existing methods lack precise localization and intent analysis.

    Purpose of the Study:

    • To define and categorize data visualization tampering techniques.
    • To present VizDefender, a novel framework for tampering detection and analysis.
    • To evaluate the effectiveness of the proposed framework.

    Main Methods:

    • Developed a semi-fragile watermark module for precise tampered region localization.
    • Integrated an intent analysis module using Multimodal Large Language Models (MLLMs).
  • Conducted extensive evaluations and user studies.

    • Main Results:

    • The semi-fragile watermark accurately localizes tampered regions while preserving visual quality.
    • The MLLM-based module effectively interprets manipulation and infers attacker intent.
    • VizDefender demonstrated high effectiveness in detecting and analyzing visualization tampering.

    Conclusions:

    • VizDefender offers a robust solution for safeguarding data visualization integrity.
    • The framework enhances trust in visual data by detecting deceptive manipulations.
    • Future work can explore advanced MLLM applications in data security.