Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications

• URL: http://arxiv.org/abs/2403.17787v2
• Date: Mon, 10 Jun 2024 10:07:24 GMT
• Title: Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications
• Authors: Fouad Trad, Ali Chehab,
• Abstract summary: Large Multimodal Models (LMMs) are designed to interpret and analyze complex data by integrating multiple modalities such as text and images. This paper investigates the applicability and effectiveness of prompt-engineered LMMs that process both images and text, compared to fine-tuned Vision Transformer (ViT) models. For the visually non-evident task, the results highlight a significant divergence in performance, with ViT models achieving F1-scores of 97.11% in predicting 25 malware classes and 97.61% in predicting 5 malware families.
• Score: 2.8161155726745237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of Large Language Models (LLMs) has led to a parallel rise in the development of Large Multimodal Models (LMMs), which have begun to transform a variety of applications. These sophisticated multimodal models are designed to interpret and analyze complex data by integrating multiple modalities such as text and images, thereby opening new avenues for a range of applications. This paper investigates the applicability and effectiveness of prompt-engineered LMMs that process both images and text, including models such as LLaVA, BakLLaVA, Moondream, Gemini-pro-vision, and GPT-4o, compared to fine-tuned Vision Transformer (ViT) models in addressing critical security challenges. We focus on two distinct security tasks: 1) a visually evident task of detecting simple triggers, such as small pixel variations in images that could be exploited to access potential backdoors in the models, and 2) a visually non-evident task of malware classification through visual representations. In the visually evident task, some LMMs, such as Gemini-pro-vision and GPT-4o, have demonstrated the potential to achieve good performance with careful prompt engineering, with GPT-4o achieving the highest accuracy and F1-score of 91.9\% and 91\%, respectively. However, the fine-tuned ViT models exhibit perfect performance in this task due to its simplicity. For the visually non-evident task, the results highlight a significant divergence in performance, with ViT models achieving F1-scores of 97.11\% in predicting 25 malware classes and 97.61\% in predicting 5 malware families, whereas LMMs showed suboptimal performance despite iterative prompt improvements. This study not only showcases the strengths and limitations of prompt-engineered LMMs in cybersecurity applications but also emphasizes the unmatched efficacy of fine-tuned ViT models for precise and dependable tasks.

Related papers

• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings. EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models [76.94378391979228]
  We introduce a new, more demanding task known as Interleaved Image-Text (IITC) This task challenges models to discern and disregard superfluous elements in both images and text to accurately answer questions. In support of this task, we further craft a new VEGA dataset, tailored for the IITC task on scientific content, and devised a subtask, Image-Text Association (ITA)
  arXiv  Detail & Related papers  (2024-06-14T17:59:40Z)
• Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models [26.322856874796702]
  Vision transformers (ViTs) struggle to capture fine-grained details from less prominent objects, charts, and embedded text. We extend recent high-resolution and multi-crop techniques by not only preserving the native resolution, but zooming in beyond it. This enhancement allows our model to better capture fine-grained details, overcoming the limitations of current ViTs.
  arXiv  Detail & Related papers  (2024-06-03T04:17:12Z)
• An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training [51.622652121580394]
  Masked image modeling (MIM) pre-training for large-scale vision transformers (ViTs) has enabled promising downstream performance on top of the learned self-supervised ViT features. In this paper, we question if the textitextremely simple lightweight ViTs' fine-tuning performance can also benefit from this pre-training paradigm. Our pre-training with distillation on pure lightweight ViTs with vanilla/hierarchical design ($5.7M$/$6.5M$) can achieve $79.4%$/$78.9%$ top-1 accuracy on ImageNet-1
  arXiv  Detail & Related papers  (2024-04-18T14:14:44Z)
• Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [47.162575147632396]
  Transferable Visual Prompting (TVP) is a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model. We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts.
  arXiv  Detail & Related papers  (2024-04-17T09:39:07Z)
• Hierarchical Side-Tuning for Vision Transformers [33.536948382414316]
  Fine-tuning pre-trained Vision Transformers (ViTs) has showcased significant promise in enhancing visual recognition tasks. PETL has shown potential for achieving high performance with fewer parameter updates compared to full fine-tuning. This paper introduces Hierarchical Side-Tuning (HST), an innovative PETL method facilitating the transfer of ViT models to diverse downstream tasks.
  arXiv  Detail & Related papers  (2023-10-09T04:16:35Z)
• FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks [129.49630356651454]
  We propose a novel FAshion-focused Multi-task Efficient learning method for Vision-and-Language tasks (FAME-ViL) Our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models.
  arXiv  Detail & Related papers  (2023-03-04T19:07:48Z)
• Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks [86.66733026149892]
  We propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-gnostic tasks. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.
  arXiv  Detail & Related papers  (2022-11-17T18:59:52Z)
• Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning [10.29251906347605]
  We propose a novel mask-guided vision transformer (MG-ViT) to achieve an effective and efficient few-shot learning on vision transformer (ViT) model. The MG-ViT model significantly improves the performance when compared with general fine-tuning based ViT models.
  arXiv  Detail & Related papers  (2022-05-20T07:25:33Z)
• Pruning Self-attentions into Convolutional Layers in Single Path [89.55361659622305]
  Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. We propose Single-Path Vision Transformer pruning (SPViT) to efficiently and automatically compress the pre-trained ViTs. Our SPViT can trim 52.0% FLOPs for DeiT-B and get an impressive 0.6% top-1 accuracy gain simultaneously.
  arXiv  Detail & Related papers  (2021-11-23T11:35:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.