In recommendation-driven online media, creators increasingly suffer from semantic mutation, where malicious secondary edits preserve visual fidelity while altering the intended meaning. Detecting these mutations requires modeling a creator's unique semantic manifold. However, training robust detector models for individual creators is challenged by data scarcity, as a distinct blogger may typically have fewer than 50 representative samples available for training. We propose quantum-enhanced blogger anomaly recognition (Q-BAR), a hybrid quantum-classical framework that leverages the high expressivity and parameter efficiency of variational quantum circuits to detect semantic anomalies in low-data regimes. Unlike classical deep anomaly detectors that often struggle to generalize from sparse data, our method employs a parameter-efficient quantum anomaly detection strategy to map multimodal features into a Hilbert space hypersphere. On a curated dataset of 100 creators, our quantum-enhanced approach achieves robust detection performance with significantly fewer trainable parameters compared to classical baselines. By utilizing only hundreds of quantum parameters, the model effectively mitigates overfitting, demonstrating the potential of quantum machine learning for personalized media forensics.

翻译：在推荐驱动的在线媒体中，创作者日益面临语义突变问题，即恶意二次编辑在保持视觉保真度的同时篡改原意。检测此类突变需对创作者的独特语义流形进行建模。然而，为个体创作者训练鲁棒的检测模型受到数据稀缺的挑战，因为特定博主通常仅有少于50个代表性训练样本可用。我们提出量子增强博主异常识别（Q-BAR），这是一种混合量子-经典框架，利用变分量子电路的高表达性与参数效率，在低数据场景下检测语义异常。相较于经典深度异常检测器常难以从稀疏数据泛化，本方法采用参数高效的量子异常检测策略，将多模态特征映射至希尔伯特空间超球面。在包含100位创作者的定制数据集上，我们的量子增强方法以显著少于经典基线的可训练参数实现了鲁棒的检测性能。通过仅使用数百个量子参数，该模型有效缓解了过拟合，展现了量子机器学习在个性化媒体取证领域的潜力。