Summary of Evaluating the Efficacy Of Prompt-engineered Large Multimodal Models Versus Fine-tuned Vision Transformers in Image-based Security Applications, by Fouad Trad and Ali Chehab
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications
by Fouad Trad, Ali Chehab
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 explores the potential of Large Multimodal Models (LMMs) to address critical security challenges, specifically detecting simple triggers and malware classification. The authors compare LMMs like LLaVA, BakLLaVA, Moondream, Gemini-pro-vision, and GPT-4o with fine-tuned Vision Transformer (ViT) models in two distinct tasks. In the visually evident task of detecting simple triggers, some LMMs demonstrated good performance with careful prompt engineering, while ViT models achieved perfect results due to simplicity. However, for the visually non-evident task of malware classification, ViT models outperformed LMMs, achieving F1-scores of 97.11% and 97.61%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates Large Multimodal Models (LMMs) that can process both images and text to address security challenges. It compares these models with fine-tuned Vision Transformer (ViT) models in detecting simple triggers and malware classification. The results show that some LMMs can be effective in certain tasks, but ViT models are better suited for precise and dependable tasks. |
Keywords
» Artificial intelligence » Classification » Gemini » Gpt » Prompt » Vision transformer » Vit
