An API-Integrated CNN–RNN Framework for Scalable Deepfake Detection

An API-Integrated CNN–RNN Framework for Scalable Deepfake Detection

Rajdeep Paul1 , Biswajit Dey2

1Department of Computer Application, Techno India University, West Bengal-700091, India

2Department of Computer Application, Techno India University, West Bengal-700091, India

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Deepfake technology has been rapidly advancing, posing significant threats to media authenticity, cybersecurity, and public trust. It's a serious threat to identity verification in the digital medium. To tackle and solve this serious problem a deepfake detection approach is taken. A Convolutional Neural Network (CNN) algorithm named Resnext and a Recurrent Neural Network (RNN) algorithm named Long Term Short Memory (LTSM) is used to train a deepfake detection model. The whole approach and process is discussed.  The model accuracy obtained is 91% using Celeb-Df dataset, Then the integration concept is discussed and how we can use this model as an Application Programming Interface (API) service for platforms or users. The model can be accessed through API to detect deepfakes and provide accurate output to validate authenticity of digital content. This work not only proposes a deepfake detection solution but also tries to practically implement the deepfake detection research outcome for real world use cases.

Key Words:  Deepfake detection, CNN, RNN, API integration, ResNext, LTSM