Adversarial Video Promotion Against Text-to-Video Retrieval
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
https://github.com/michaeltian108/ViPro.
689b413cfab6fdd2e52ac6d0
Qiwei Tian
claimed_verified
2025-08-13T07:14:26.314Z
689b16601133f93a6f4afdca
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/MWlw4vtHV6ptOpTT6-U57.png
Qiwei Tian
user
michaeltqw108
689b413cfab6fdd2e52ac6d1
Chenhao Lin
689b413cfab6fdd2e52ac6d2
Zhengyu Zhao
689b413cfab6fdd2e52ac6d3
Qian Li
689b413cfab6fdd2e52ac6d4
Shuai Liu
689b413cfab6fdd2e52ac6d5
Chao Shen
Adversarial Video Promotion Against Text-to-Video Retrieval
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
https://github.com/michaeltian108/ViPro.
The Video Promotion attack (ViPro) enhances the robustness of text-to-video retrieval (T2VR) by promoting videos towards selected queries, demonstrating significant improvements over existing baselines in various attack scenarios.
cross-modal models text-to-video retrieval T2VR Video Promotion attack ViPro Modal Refinement MoRe black-box transferability multi-target setting white-box grey-box black-box defences imperceptibility