• Aiyaz Anwar Qureshi
• Rajan Deshmukh
• Nida Gulamnabi Shaikh
• Abutalha Fateh Sheikh
• Ajinkya Suresh Khobragade

In today's era of rapid AI advancement, ensuring the authenticity of digital content is crucial across various sectors. This project focuses on devising a robust methodology to differentiate between genuine human-generated content and AI-generated content, which poses challenges due to sophisticated AI models. The approach integrates machine learning, NLP, and statistical analysis, starting with a diverse dataset collected from online platforms. Key linguistic features of human-generated content will be analyzed to establish a baseline, alongside attributes unique to AI-generated content, like repetitive patterns and lack of contextual understanding. Machine learning techniques, including supervised and unsupervised algorithms, will extract discriminative features to train a model capable of discerning between real and AI-generated content. Validation will involve extensive experimentation and comparison with existing methods using metrics like precision and recall. Ethical considerations surrounding AI-generated content, including misinformation and erosion of trust, will also be investigated. This paper offers a comprehensive methodology for determining the authenticity of data, particularly focusing on distinguishing between genuine human-generated content and AI-generated content, addressing the challenges posed by the proliferation of sophisticated AI technologies.

Authenticity Verification, Data Integrity, AI-Generated Content, Natural Language Processing, Machine Learning, Ethical Implications.

"AI based Authenticity check", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l376-l381, April-2024, Available :http://www.jetir.org/papers/JETIR2404B51.pdf

"AI based Authenticity check", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl376-l381, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B51.pdf