Detection of AI-Generated Content Across Modalities: Text, Image and Audio

With rapid development in generative models, increase in AI-generated text, images, and videos calls for robust detection across these modalities. This paper aims to develop models which offer a scalable solution capable of being able to differentiate between AI-generated and human-generated content across different modes. The ethical implications of AI-generated content and their significance are also explored. The models are evaluated with publicly available datasets and achieve an accuracy of 99.48% for text, 97.52% for image and 98.46% for audio.