• Anyam Venkata Srinivas
• Vineela Veeramallu
• Neelapala Bala venkata karthik
• Sandeep Vaskuri
• Neela Vinod

The rapid growth of social media platforms like Instagram has led to an increase in fake or fraudulent user accounts, which are often used for spamming, spreading misinformation, or manipulating public opinion. Detecting such fake profiles has become a critical challenge in the field of cybersecurity and data integrity. This research paper presents a machine learning-based approach for detecting fake Instagram profiles using an Artificial Neural Network (ANN). The model is trained on a dataset of manually labeled Instagram accounts, using features such as follower-following ratio, profile picture availability, bio content, post count, and engagement metrics. After preprocessing and feature engineering, the data is fed into an ANN model designed to classify accounts as either genuine or fake. The model demonstrates promising accuracy and precision, showing the potential of neural networks in automated social media moderation. This research highlights the role of AI in enhancing online trust and safety, and it opens avenues for further work in improving the robustness and generalizability of fake account detection systems.

Fake profile detection, Instagram, Artificial Neural Network (ANN), Social media security, Machine learning, Deep learning, Classification model, Online fraud detection, Feature engineering, Fake account identification.

"Detection of Fake Instagram Profiles Using Artificial Neural Networks: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.i764-i768, March-2025, Available :http://www.jetir.org/papers/JETIR2503899.pdf

"Detection of Fake Instagram Profiles Using Artificial Neural Networks: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppi764-i768, March-2025, Available at : http://www.jetir.org/papers/JETIR2503899.pdf