  Recent studies have revealed that vision-language (VL) models are vulnerable
to adversarial attacks for image-text retrieval (ITR). However, existing
defense strategies for VL models primarily focus on zero-shot image
classification, which do not consider the simultaneous manipulation of image
and text, as well as the inherent many-to-many (N:N) nature of ITR, where a
single image can be described in numerous ways, and vice versa. To this end,
this paper studies defense strategies against adversarial attacks on VL models
for ITR for the first time. Particularly, we focus on how to leverage the N:N
relationship in ITR to enhance adversarial robustness. We found that, although
adversarial training easily overfits to specific one-to-one (1:1) image-text
pairs in the train data, diverse augmentation techniques to create one-to-many
(1:N) / many-to-one (N:1) image-text pairs can significantly improve
adversarial robustness in VL models. Additionally, we show that the alignment
of the augmented image-text pairs is crucial for the effectiveness of the
defense strategy, and that inappropriate augmentations can even degrade the
model's performance. Based on these findings, we propose a novel defense
strategy that leverages the N:N relationship in ITR, which effectively
generates diverse yet highly-aligned N:N pairs using basic augmentations and
generative model-based augmentations. This work provides a novel perspective on
defending against adversarial attacks in VL tasks and opens up new research
directions for future work.
