Enhancing Adversarial Transferability of Visual-Language Pre-training Models through Collaborative Multimodal Interaction

Core Concepts

Modality interaction is crucial for improving adversarial transferability in Visual-Language Pre-training models.

Abstract

Despite advancements in Vision-Language Pre-training (VLP) models, susceptibility to adversarial attacks remains a challenge. The proposed Collaborative Multimodal Interaction Attack (CMI-Attack) leverages modality interaction through embedding guidance and image gradient enhancement. This novel attack significantly improves transfer success rates on the Flickr30K dataset compared to state-of-the-art methods. Existing work overlooks the importance of modality interaction, limiting adversarial transferability. Modality interaction enhances the effectiveness of adversarial attacks by leveraging mutual influences between vision and text. The study explores the impact of modality interactions during attacks on VLP models, revealing their pivotal role in bolstering attack efficacy.

Stats

CMI-Attack raises transfer success rates from ALBEF to TCL, CLIPViT, and CLIPCNN by 8.11%-16.75% over state-of-the-art methods.
SGA first explores black-box attacks on VLP models and significantly improves the transferability of adversarial examples.
Incorporating image gradients during text generation enhances the transferability of adversarial examples.

Quotes

"In essence, modality interaction refers to the interaction and information exchange between different modalities."
"Our work addresses the underexplored realm of transfer attacks on VLP models, shedding light on the importance of modality interaction for enhanced adversarial robustness."

Key Insights Distilled From

Improving Adversarial Transferability of Visual-Language Pre-training Models through Collaborative Multimodal Interaction

by Jiyuan Fu,Zh... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10883.pdf

Deeper Inquiries

How can modality interaction be further optimized to enhance adversarial robustness beyond current methods

To further optimize modality interaction for enhancing adversarial robustness beyond current methods, researchers can explore several avenues. One approach could involve incorporating more sophisticated techniques for cross-modal alignment, such as leveraging attention mechanisms to capture intricate relationships between visual and textual features. Additionally, introducing dynamic fusion strategies that adaptively combine information from different modalities based on the context of the input data could enhance the robustness of VLP models against adversarial attacks. Furthermore, exploring novel ways to incorporate multimodal feedback loops during training to reinforce the alignment between modalities and improve model generalization could also be beneficial.

What are potential drawbacks or limitations of relying heavily on modality interactions for improving VLP model security

While relying heavily on modality interactions can significantly enhance VLP model security and adversarial robustness, there are potential drawbacks and limitations to consider. One limitation is the increased complexity introduced by intricate modality interactions, which may lead to higher computational costs during training and inference. Moreover, over-reliance on modality interactions alone may make VLP models more susceptible to adversarial attacks targeting these specific interaction patterns. Additionally, focusing solely on modality interactions for improving security may overlook other crucial aspects of model robustness, such as data diversity and regularization techniques.

How might understanding modal interactions in VLP models contribute to advancements in other fields beyond machine learning

Understanding modal interactions in VLP models has broader implications beyond machine learning that can contribute to advancements in various fields:

Human-Computer Interaction: Insights into how different modalities interact within VLP models can inform the design of more intuitive human-computer interfaces that leverage natural language understanding alongside visual cues.
Cognitive Science: Studying modal interactions in VLP models can provide valuable insights into how humans process multimodal information and improve our understanding of cognitive processes related to perception and communication.
Robotics: Applying knowledge of modal interactions in VLP models can enhance robotic systems' ability to understand and respond to complex commands involving both text-based instructions and visual inputs.
Healthcare: Leveraging multimodal understanding from VLP models can aid healthcare professionals in analyzing medical images alongside patient records or reports for more accurate diagnosis and treatment planning.