Towards Adversarial Attack on Vision-Language Pre-training Models (2206.09391v2)

Abstract: While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain largely unexplored. This paper studied the adversarial attack on popular VLP models and V+L tasks. First, we analyzed the performance of adversarial attacks under different settings. By examining the influence of different perturbed objects and attack targets, we concluded some key observations as guidance on both designing strong multimodal adversarial attack and constructing robust VLP models. Second, we proposed a novel multimodal attack method on the VLP models called Collaborative Multimodal Adversarial Attack (Co-Attack), which collectively carries out the attacks on the image modality and the text modality. Experimental results demonstrated that the proposed method achieves improved attack performances on different V+L downstream tasks and VLP models. The analysis observations and novel attack method hopefully provide new understanding into the adversarial robustness of VLP models, so as to contribute their safe and reliable deployment in more real-world scenarios. Code is available at https://github.com/adversarial-for-goodness/Co-Attack.

Understanding Adversarial Vulnerabilities in Vision-Language Pre-training Models

Abstract Overview

The paper "Towards Adversarial Attack on Vision-Language Pre-training Models" examines the adversarial robustness of Vision-Language Pre-training (VLP) models—a domain in which systematic paper was lacking. The research focuses on adversarial attacks against these pre-trained models representing multimodal tasks, using popular architectures such as ALBEF, TCL, and CLIP. It not only investigates how different attack configurations affect adversarial performance but innovatively proposes a Collaborative Multimodal Adversarial Attack (Co-Attack) method that simultaneously perturbs multiple modality inputs. This novel approach effectively strengthens adversarial performance, potentially enhancing model safety in real-world applications.

Key Metrics and Bold Claims

The paper systematically explores multiple attack configurations based on input perturbations (image, text, or both) and target embeddings (unimodal or multimodal embeddings). Significant findings include the observation that perturbing both image and text inputs (bi-modal perturbation) consistently results in a stronger adversarial attack than single-modal perturbations, reflecting a substantial vulnerability when both modalities are targeted collaboratively. Moreover, Co-Attack, when tested against robust baseline methods, demonstrated superior performance across various VLP models. Importantly, the statistical evaluation affirmed that Co-Attack generates a larger resultant perturbation within the embedding space, indicating a significant level of strategic advantage.

Implications and Future Directions

This paper's contributions have notable theoretical and practical implications. The insights derived from this analysis pave the way for establishing security protocols in deploying multimodal learning models in sensitive environments that require adversarial robustness. Additionally, the implementation of Co-Attack holds potential for demonstrably improving adversarial research and may inspire novel defense strategies or robust architecture designs incorporating complexity beyond unimodal attacks.

The paper emphasizes the need to deliberate on collaborative perturbation strategies in enabling adversarial maneuvers—an aspect likely to evolve within AI safety and security discourse. Going forward, researchers could focus on extending this framework to other pre-training models, considering varied adversarial conditions and modalities beyond simple vision-language integration, encompassing speech, tactile data, and even multivariate sensor inputs.

Conclusion

Through meticulous experimentation with VLP models, this research sheds light on the adversarial vulnerabilities within multimodal frameworks. By providing thoughtfully formulated adversarial tactics like Co-Attack, it not only advances understanding but sets forth a tactical frontier for further exploration in adversarial learning and defense mechanisms. The efforts to bolster model reliability and robustness across diversified AI applications signify a constructive stride toward securing artificial intelligence spectrums against perturbative threats and ensuring more deployable, resilient AI solutions in complex real-world scenarios.