Few-shot multilabel multiclass continuous learning 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, as well as continuously having newer classes due to the rapidly augmenting amount of criminals in Darkweb. While many methods have been proposed to classify this image content, multi-label multi-class continuous learning classification remains under explored. 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. Furthermore, we demonstrate the most appropriate continuous learning methodology to overcome with the challenges of accessing old data and training over and over again for each newly added class. Finally, we compare the performance with multi-label methodologies applied to CLIP, leading multi-label methods and the continuous learning approaches.