Summary of Robust Clip: Unsupervised Adversarial Fine-tuning Of Vision Embeddings For Robust Large Vision-language Models, by Christian Schlarmann et al.
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
by Christian Schlarmann, Naman Deep Singh, Francesco Croce, Matthias Hein
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 This paper tackles the pressing issue of ensuring robustness in large multi-modal foundation models, specifically those used for real-world tasks. Current models like OpenFlamingo, LLaVA, and GPT-4 are vulnerable to adversarial attacks on the vision modality, which can be exploited to spread misinformation or defraud users. To address this, researchers propose an unsupervised adversarial fine-tuning scheme to obtain a robust CLIP vision encoder, achieving robustness across various down-stream tasks, including zero-shot classification and LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep our online world safe by making sure large AI models can’t be tricked into spreading fake news or stealing money. The problem is that these powerful models are super easy to hack using fake images. To fix this, the researchers came up with a clever way to make the model more resistant to attacks. This means users of those AI models will be protected from bad guys trying to manipulate them. |
Keywords
* Artificial intelligence * Classification * Encoder * Fine tuning * Gpt * Multi modal * Unsupervised * Zero shot
