TLDR: A new research paper introduces the Local Shuffle and Sample-based Attack (LSSA), a novel method designed to create more transferable adversarial examples for Visual-Language Pre-training (VLP) models. Unlike previous attacks that suffered from overfitting due to limited input diversity, LSSA uses local image block shuffling to enhance image diversity while preserving spatial information, and a sample-based augmentation strategy to craft adversarial texts using both original and sampled images. This approach significantly improves the ability of adversarial examples to fool various VLP models and Large Vision-Language Models (LVLMs) across different tasks, providing crucial insights into model robustness.
Visual-Language Pre-training (VLP) models have become incredibly powerful, excelling in a wide range of tasks that combine images and text, such as retrieving images based on text descriptions or generating captions for pictures. However, despite their impressive capabilities, these models are not immune to a type of vulnerability known as adversarial examples. These are subtly altered inputs—like a slightly modified image or text—that can trick a model into making incorrect predictions.
A significant challenge in this area is improving the ‘transferability’ of these adversarial examples. This means creating an adversarial example for one VLP model that can also successfully fool other, different VLP models, even if the attacker doesn’t know the internal workings of those other models (a ‘black-box’ attack). Previous attempts to enhance this transferability often ran into a problem called ‘overfitting.’ This happened because these methods relied too heavily on information from adversarial examples in one modality (e.g., an altered image) when trying to create attacks in another (e.g., altered text), leading to a lack of diversity in the attack inputs.
Introducing LSSA: A New Approach to Adversarial Attacks
To overcome these limitations, researchers have introduced a novel attack method called the Local Shuffle and Sample-based Attack (LSSA). This approach draws inspiration from strategies used in adversarial training, which typically aims to make models more robust. LSSA, however, uses these insights to generate more effective and transferable adversarial examples.
The core idea behind LSSA is to increase the diversity of the inputs used to craft adversarial examples while carefully preserving crucial information. It does this through two main mechanisms:
Local Shuffle Transformation: Instead of completely scrambling an image, which can disrupt important spatial information that multimodal tasks rely on, LSSA randomly shuffles only one small, local block within an image. This subtle change expands the variety of image-text pairs used to generate adversarial images. This method helps maintain the image’s overall structure while still introducing enough diversity to make the adversarial examples more transferable.
Sample-based Augmentation: When it comes to generating adversarial text, LSSA takes a more comprehensive approach. Instead of just using the original image or a single adversarial image, it samples the neighborhoods around the generated adversarial images. This means it considers multiple slightly varied versions of the adversarial image, along with the original image and text, to craft the adversarial text. By leveraging this richer set of information, LSSA ensures that the adversarial text is significantly different from both the original and adversarial image features, further boosting transferability.
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Demonstrated Effectiveness Across Models and Tasks
Extensive experiments have shown that LSSA significantly enhances the transferability of multimodal adversarial examples. It has been tested on multiple VLP models and datasets, consistently outperforming other advanced attack methods in black-box settings. For instance, in image-text retrieval tasks, LSSA showed notable improvements in attack success rates when transferring adversarial examples between different VLP models like ALBEF and TCL, or CLIPViT and CLIPCNN.
Beyond image-text retrieval, LSSA also proved effective in cross-task transferability, meaning adversarial examples crafted for one task (like retrieval) could successfully attack models designed for other tasks, such as image captioning and visual grounding. This suggests that the adversarial perturbations generated by LSSA carry more robust spatial adversarial information.
Notably, LSSA is also the first work to evaluate multimodal adversarial transferability performance on Large Vision-Language Models (LVLMs), such as BLIP-2, VisualGLM, MiniGPT4, and PandaGPT. Even against these powerful models, LSSA demonstrated superior attack performance compared to existing baselines, highlighting its effectiveness and robustness. For more technical details, you can refer to the full paper here.
In conclusion, LSSA addresses the overfitting issue in previous multimodal adversarial attacks by introducing a novel combination of local image shuffling and sample-based text augmentation. This method not only improves the diversity of inputs but also preserves critical spatial information, leading to more transferable and potent adversarial examples across a wide range of VLP models and downstream tasks, including the latest LVLMs. This research provides valuable insights into the vulnerabilities of these advanced AI systems and can inspire future work in developing more robust and secure visual-language models.
