Introduction: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. Methods: We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Results: Our model can achieve 74\% accuracy for images and 98\% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Conclusions: Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data.

翻译：摘要：引言：隐蔽的烟草广告常引发监管措施。本文表明，人工智能，特别是深度学习，在检测隐性广告方面具有巨大潜力，并可实现对烟草相关媒体内容的无偏、可重复且公平的量化。方法：我们提出了一种基于深度学习、生成式方法与人工强化的文本与图像集成处理模型，该模型即使在可用训练数据极少的情况下，也能检测文本和视觉形式中的吸烟案例。结果：我们的模型在图像上可达到74%的准确率，在文本上可达98%。此外，我们的系统整合了以人工强化形式进行专家干预的可能性。结论：利用深度学习提供的预训练多模态、图像及文本处理模型，即便使用少量训练数据，也能检测不同媒体中的吸烟行为。