Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content.