Few-Shot Multi-Label Multi-Class Classification for Dark Web Image Categorization

Categorizing dark web image content is critical for identifying and averting potential threats. However, this remains a challenge due to the nature of the data, which includes multiple co-existing domains and intra-class variations. While many methods have been proposed to classify this image content, multi-label multi-class classification remains underexplored. In this paper, we propose a novel and efficient strategy for transforming a zero-shot single-label classifier into a few-shot multi-label classifier. This approach combines a label empowering methodology with few-shot data. We use CLIP, a conservative learning model that uses image-text pairs, to demonstrate the effectiveness of our strategy. Finally, we compare our method's performance with other multi-label methodologies applied to CLIP and other leading multi-label architectures.