Summary of Multimodal Adversarial Defense For Vision-language Models by Leveraging One-to-many Relationships, By Futa Waseda et al.
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships
by Futa Waseda, Antonio Tejero-de-Pablos, Isao Echizen
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multimodal adversarial training (MAT) method is designed to defend vision-language (VL) models against attacks that target both images and texts. Unlike existing defense methods that focus on image classification, MAT incorporates adversarial perturbations in both modalities during training, leading to improved robustness. The approach also addresses the limitations of current VL defenses by leveraging one-to-many relationships between images and texts. Experimental results demonstrate the effectiveness of MAT across various VL models and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can understand both pictures and words! But right now, these “vision-language” (VL) models are very bad at defending themselves against sneaky attacks that try to trick them. This paper introduces a new way to make VL models stronger by training them to resist both picture and text attacks at the same time. The idea is to use fake pictures and texts during training to make the model more resilient. It’s like practicing self-defense in a game! |
Keywords
» Artificial intelligence » Image classification
