Comparative Evaluation of Deep Learning Architectures and Hybrid Heuristics for Automated Gambling Content Detection

The exponential proliferation of online gambling content represents a multifaceted challenge for contemporary automated content moderation systems, primarily driven by the sophisticated visual obfuscation and semantic complexity characteristic of modern digital advertising. This study conducts a rigorous comparative evaluation of the efficacy of Deep Learning (DL) architectures against classical Machine Learning (ML) paradigms for the deterministic identification of gambling-related imagery. Specifically, we propose and implement GADIA (Gambling Ad Detector with Intelligent Analysis), a novel hybrid funnel-based architecture that integrates structural heuristic filtering with an asymmetrically fine-tuned ResNet50 classifier. To address the systemic scarcity of high-quality public repositories, the models were trained and validated on a proprietary, strictly balanced dataset of 2,312 images, meticulously curated to encapsulate real-world adversarial marketing techniques. Performance bench-marks were established through Accuracy, Precision, Recall, F1-score, and AUC metrics. Experimental evidence demonstrates that the ResNet50 architecture attained a superior robustness profile, achieving 85.01% accuracy and 90.42% recall, significantly outperforming traditional baselines that failed to capture high-dimensional visual hierarchies. These findings validate that deep residual learning, when integrated into a hybrid heuristic-visual pipeline, provides a computationally efficient and scalable foundation for real-time platform governance and digital safety monitoring.