Countering the Rise of AI-Generated Content with Innovative Detection Strategies and Large Language Models
J Gowrishankar, Shashikant Deepak, Manish Srivastava · 2024
The necessity for novel detection algorithms is of the utmost importance at a time when AI-generated content is ubiquitous. The suggested AI-Guardian methodology provides a novel strategy to combat the proliferation of AI-generated material. This approach uses state-of-the-art algorithms and big language models to tell the difference between AI-generated and human-authored text. The capacity of AI-Guardian to assess language patterns, contextual coherence, and semantic consistency in text is what sets it apart. By creating contextual embeddings and assessing contextual similarities, it provides a thorough comprehension of content's veracity. As a result of this comprehensive contextual analysis, AI-Guardian is able to achieve an impressive 94% detection accuracy. Also, the false positive and false negative rates of AI-Guardian are very low. Its success in content identification is shown by high metrics such as accuracy, recall, F1 score, specificity, and area under the curve (AUC). The comprehensive approach used by AI-Guardian guarantees accurate identification of AI-generated material without compromising accuracy or leading to the accidental deletion of genuine information. AI -Guardian is a dependable lighthouse in the sea of AI-generated content, ushering in a new era of excellence in the field of content identification. It is the go-to solution for protecting against the potentially harmful effects of AI-generated material due to its superior performance metrics and thorough review standards.