Summary of Ai Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning, by Maisha Binte Rashid and Pablo Rivas
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning
by Maisha Binte Rashid, Pablo Rivas
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
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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 paper presents an effective strategy to enhance the robustness of multimodal image captioning models against adversarial attacks. By leveraging the Fast Gradient Sign Method (FGSM) to generate adversarial examples and incorporating adversarial training techniques, the authors demonstrate improved model robustness on two benchmark datasets: Flickr8k and COCO. The findings indicate that selectively training only the text decoder of the multimodal architecture shows performance comparable to full adversarial training while offering increased computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make AI systems safer by teaching them to resist attacks. The authors use a technique called FGSM to create fake examples that can trick image captioning models, then they train those models to be more robust against these attacks. They test their approach on two big datasets and find that it works well, even when only training part of the model. This is important because it shows how we can make AI systems safer without sacrificing performance. |
Keywords
* Artificial intelligence * Decoder * Image captioning
