• Dr. Savita Sangam
• Ayush Gaikar
• Siddhi Chavan
• Prachi Kokate

The worries regarding the validity and reliability of reports generated digitally are growing along with the capabilities of artificial intelligence (AI). This study offers a thorough approach for identifying AI-generated reports and reducing the hazards that could arise from their widespread use. Our method examines textual content for telltale signals of AI production by combining machine learning algorithms, statistical analysis, and natural language processing approaches. By leveraging a variety of datasets that include real and artificial intelligence-generated reports, we utilize a multi-tiered detection approach that combines linguistic analysis, anomaly detection, and semantic comprehension. In addition, we investigate how well deep learning architectures—such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs)—work to improve the precision and resilience of our detection system. Through comprehensive testing and assessments, we show that our method effectively reduces false positives while correctly identifying AI-generated reports from authentic ones. We also talk about the wider implications of our research in preventing the spread of fake news and maintaining the credibility of online reporting services. In the current information ecosystem, AI-generated media poses difficulties that require identification and resolution. This study adds to the ongoing attempts to find effective ways for doing so.

AI-generated reports, Detection system, Artificial intelligence, Report detection, Report Authenticaion

"AI GENERATED REPORT DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.158-162, June-2024, Available :http://www.jetir.org/papers/JETIRGH06026.pdf

"AI GENERATED REPORT DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp158-162, June-2024, Available at : http://www.jetir.org/papers/JETIRGH06026.pdf