The document presents an incremental learning framework for image spam filtering, addressing the evolving and diverse nature of image spam through a novel density-based clustering filter combined with machine learning classifiers. The proposed framework enhances detection capacity while ensuring adaptability and precision, achieving high filtering accuracy with a low false positive rate. Experiments demonstrated its effectiveness, highlighting the need for robust strategies in countering sophisticated spamming techniques.