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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Uncovering Vision Modality Threats in Image-to-Image Tasks
06 Dec 2024
Abstract
Current image generation models can effortlessly produce high-quality, highly
realistic images, but this also increases the risk of misuse. In various
Text-to-Image or Image-to-Image tasks, attackers can generate a series of
images containing inappropriate content by simply editing the language modality
input. Currently, to prevent this security threat, the various guard or defense
methods that are proposed also focus on defending the language modality.
However, in practical applications, threats in the visual modality,
particularly in tasks involving the editing of real-world images, pose greater
security risks as they can easily infringe upon the rights of the image owner.
Therefore, this paper uses a method named typographic attack to reveal that
various image generation models also commonly face threats in the vision
modality. Furthermore, we also evaluate the defense performance of various
existing methods when facing threats in the vision modality and uncover their
ineffectiveness. Finally, we propose the Vision Modal Threats in Image
Generation Models (VMT-IGMs) dataset, which would serve as a baseline for
evaluating the vision modality vulnerability of various image generation
models.
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Details
- Title
- Uncovering Vision Modality Threats in Image-to-Image Tasks
- Creators
- Hao ChengErjia XiaoJiayan YangJiahang CaoQiang ZhangJize ZhangKaidi XuJindong GuRenjing Xu
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021985103404721