Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques

• URL: http://arxiv.org/abs/2407.11121v1
• Date: Mon, 15 Jul 2024 18:00:01 GMT
• Title: Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
• Authors: Rishika Bhagwatkar, Shravan Nayak, Reza Bayat, Alexis Roger, Daniel Z Kaplan, Pouya Bashivan, Irina Rish,
• Abstract summary: Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks.
• Score: 12.907116223796201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. However, as they are becoming increasingly prevalent, ensuring their robustness against adversarial attacks is paramount. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks. Additionally, we introduce novel, cost-effective approaches to enhance robustness through prompt formatting. By rephrasing questions and suggesting potential adversarial perturbations, we demonstrate substantial improvements in model robustness against strong image-based attacks such as Auto-PGD. Our findings provide important guidelines for developing more robust VLMs, particularly for deployment in safety-critical environments.

Related papers

• Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks [34.40254709148148]
  Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding. Their potential safety and robustness issues raise concerns that adversaries may evade the system and cause these models to generate toxic content through malicious attacks. We present Chain of Attack (CoA), which iteratively enhances the generation of adversarial examples based on the multi-modal semantic update.
  arXiv  Detail & Related papers  (2024-11-24T05:28:07Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography [21.632703081999036]
  Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems. We propose to leverage typographic attacks against AD systems relying on the decision-making capabilities of Vision-LLMs.
  arXiv  Detail & Related papers  (2024-05-23T04:52:02Z)
• Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors [31.383591942592467]
  Vision-language models (VLMs) offer innovative ways to combine visual and textual data for enhanced understanding and interaction. Patch-based adversarial attack is considered the most realistic threat model in physical vision applications. We introduce SmoothVLM, a defense mechanism rooted in smoothing techniques, to protectVLMs from the threat of patched visual prompt injectors.
  arXiv  Detail & Related papers  (2024-05-17T04:19:19Z)
• Adversarial Attacks and Defense for Conversation Entailment Task [0.49157446832511503]
  Large language models are vulnerable to low-cost adversarial attacks. We fine-tune a transformer model to accurately discern the truthfulness of hypotheses. We introduce an embedding perturbation loss method to bolster the model's robustness.
  arXiv  Detail & Related papers  (2024-05-01T02:49:18Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation [56.622250514119294]
  In contrast to white-box adversarial attacks, transfer attacks are more reflective of real-world scenarios. We propose a self-augment-based transfer attack method, termed SA-Attack.
  arXiv  Detail & Related papers  (2023-12-08T09:08:50Z)
• Adversarial Prompt Tuning for Vision-Language Models [86.5543597406173]
  Adversarial Prompt Tuning (AdvPT) is a technique to enhance the adversarial robustness of image encoders in Vision-Language Models (VLMs) We demonstrate that AdvPT improves resistance against white-box and black-box adversarial attacks and exhibits a synergistic effect when combined with existing image-processing-based defense techniques.
  arXiv  Detail & Related papers  (2023-11-19T07:47:43Z)
• JAB: Joint Adversarial Prompting and Belief Augmentation [81.39548637776365]
  We introduce a joint framework in which we probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation. This framework utilizes an automated red teaming approach to probe the target model, along with a belief augmenter to generate instructions for the target model to improve its robustness to those adversarial probes.
  arXiv  Detail & Related papers  (2023-11-16T00:35:54Z)
• On Evaluating Adversarial Robustness of Large Vision-Language Models [64.66104342002882]
  We evaluate the robustness of large vision-language models (VLMs) in the most realistic and high-risk setting. In particular, we first craft targeted adversarial examples against pretrained models such as CLIP and BLIP. Black-box queries on these VLMs can further improve the effectiveness of targeted evasion.
  arXiv  Detail & Related papers  (2023-05-26T13:49:44Z)

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.