Abstract
Thanks to the development of cross-modal models, text-to-video retrieval (T2VR) is advancing rapidly, but its robustness remains largely unexamined. Existing attacks against T2VR are designed to push videos away from queries, i.e., suppressing the ranks of videos, while the attacks that pull videos towards selected queries, i.e., promoting the ranks of videos, remain largely unexplored. These attacks can be more impactful as attackers may gain more views/clicks for financial benefits and widespread (mis)information. To this end, we pioneer the first attack against T2VR to promote videos adversarially, dubbed the Video Promotion attack (ViPro). We further propose Modal Refinement (MoRe) to capture the finer-grained, intricate interaction between visual and textual modalities to enhance black-box transferability. Comprehensive experiments cover 2 existing baselines, 3 leading T2VR models, 3 prevailing datasets with over 10k videos, evaluated under 3 scenarios. All experiments are conducted in a multi-target setting to reflect realistic scenarios where attackers seek to promote the video regarding multiple queries simultaneously. We also evaluated our attacks for defences and imperceptibility. Overall, ViPro surpasses other baselines by over $30/10/4\%$ for white/grey/black-box settings on average. Our work highlights an overlooked vulnerability, provides a qualitative analysis on the upper/lower bound of our attacks, and offers insights into potential counterplays. Code will be publicly available at this https URL.
Abstract (translated)
感谢跨模态模型的发展，文本到视频检索（T2VR）正在迅速进步，但其鲁棒性仍然很大程度上未被研究。现有针对T2VR的攻击旨在将视频从查询中推开，即降低视频排名；而能够将视频拉向选定查询、即提升视频排名的攻击则尚未充分探索。此类攻击可能更具影响力，因为攻击者可能会为了经济利益和广泛传播（错误）信息而获得更多点击/观看。为此，我们开创了首个旨在通过敌对方式推广视频的T2VR攻击，称为“Video Promotion”攻击（简称ViPro）。此外，我们提出了模态细化（Modal Refinement, MoRe），以捕捉视觉与文本模态之间更为细微复杂的交互作用，从而增强黑盒转移能力。全面的实验涵盖了2个现有基线、3种领先的T2VR模型以及包含超过10k视频的3大流行数据集，在3种不同场景下进行了评估。所有实验均在多目标设置中进行，以反映现实情况中的攻击者试图同时针对多个查询推广视频的情景。我们还对防御措施和不可察觉性进行了测试。总体而言，ViPro在白盒/灰盒/黑盒环境下分别平均超过了其他基线30%/10%/4%以上。我们的工作揭示了一个被忽视的漏洞，提供了对我们攻击上下限的定性分析，并为潜在的反击策略提供见解。代码将在该链接公开发布。
URL
https://arxiv.org/abs/2508.06964
