Online platforms have become integral to modern communication, with images playing a crucial role in conveying information and emotions. However, a significant portion of online imagery contains offensive or inappropriate content, such as hate speech, violence, and nudity, which can have detrimental social and psychological impacts. This research aims to address this challenge by leveraging the power of deep learning, specifically Convolutional Neural Networks (CNNs), to accurately categorize images as offensive or non-offensive. Traditional machine learning algorithms, including Support Vector Machines (SVM), Naïve Bayes, and Decision Trees, have shown limitations in achieving high accuracy in this complex task. These methods often struggle to effectively capture the intricate visual patterns and contextual nuances present in images. In contrast, CNNs, with their inherent ability to automatically learn and extract hierarchical features from images, offer a significant advantage. CNNs can effectively analyze visual elements, text overlays, and contextual cues within images to identify and classify offensive content with high precision. Our research demonstrates the effectiveness of a CNN-based model in classifying images as offensive or non-offensive with an accuracy exceeding 90%. This high level of accuracy significantly improves upon the limitations of traditional methods and has the potential to mitigate the negative social impact of offensive content on online platforms. By effectively identifying and filtering offensive imagery, our model can contribute to creating a safer and more inclusive online environment for all users.

Meme detection, Convolutional neural networks, deep learning, Offensive &; Non- Offensive.