Image Spam

Image spam, the embedding of spam content within images to evade text-based filters, is a growing concern requiring robust detection methods. Current research focuses on applying machine learning techniques, particularly convolutional neural networks (CNNs) and large language models (LLMs) like BERT and its variants, to analyze both image content and textual metadata for improved classification. These efforts aim to enhance the accuracy and efficiency of spam detection systems, ultimately improving online security and user experience by mitigating the spread of unwanted and potentially harmful content. The development of explainable AI (XAI) methods alongside these models is also gaining traction, aiming to increase transparency and trust in automated spam detection.