Q-BAR: Blogger Anomaly Recognition via Quantum-enhanced Manifold Learning

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.