The "nine-dash line" image represents an illegal maritime claim that frequently appears in digital content, posing significant risks of distorting Vietnam's territorial sovereignty. Manual moderation cannot keep pace with the rapidly growing volume of data, especially as such infringing images are often small, thin, and subtly embedded. In this study, we employ an enhanced Faster R-CNN model, incorporating knowledge distillation (KD) from the vision-language model DINOv2 to detect "nine-dash line" imagery. A KD loss function is utilized to align embeddings between predicted and ground-truth regions, thereby improving the detection of small and occluded objects. The model is trained on a specialized dataset of 836 images and achieves a mean Average Precision (mAP) of 58.4\%, representing an improvement of 6.2\% over the baseline Faster R-CNN with a ResNet-50 backbone. In addition, the employed approach achieves an Average Recall (AR) of 66.2\%, compared to 58.8\% for the baseline model. This system demonstrates strong potential for integration into automated digital content moderation workflows, supporting the protection of national sovereignty in cyberspace.