Summary of Robust Image Classification with Multi-modal Large Language Models, by Francesco Villani et al.
Robust image classification with multi-modal large language models
by Francesco Villani, Igor Maljkovic, Dario Lazzaro, Angelo Sotgiu, Antonio Emanuele Cinà, Fabio Roli
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 A novel defense mechanism called Multi-Shield is proposed to enhance the robustness of deep neural networks against adversarial examples. By combining and complementing existing defenses with multi-modal information, Multi-Shield leverages large language models to detect and abstain from uncertain classifications when there is no alignment between textual and visual representations of the input. The approach is demonstrated to outperform original defenses on CIFAR-10 and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new defense called Multi-Shield helps make deep neural networks more secure against fake examples that are designed to trick them. It combines different methods and uses language models to check if the information from text and images matches. This makes it easier to detect and reject these fake examples, and it does a better job than existing defenses on some image recognition tasks. |
Keywords
» Artificial intelligence » Alignment » Multi modal
