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Found 3 papers, 2 papers with code
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors
In this paper, we propose to address patched visual prompt injection, where adversaries exploit adversarial patches to generate target content in VLMs.
Uplift Modeling for Target User Attacks on Recommender Systems
UBA estimates the treatment effect on each target user and optimizes the allocation of fake user budgets to maximize the attack performance.
RecAD: Towards A Unified Library for Recommender Attack and Defense
Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.
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