A Review on Advancements in Deep Learning for Robust Deep Fake Detection
PG Student, Department of Computer Engineering, Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, India
Chaitanya S. Kulkarni
Associate Professor & HoD, Department of Artificial Intelligence and Data Science, Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, India
In the age of digital media, the rise of deep fake technology presents formidable challenges to the authenticity and trustworthiness of visual and auditory content. Detecting deep fakes has become imperative, necessitating robust methodologies capable of discerning between genuine and manipulated media. This paper presents a comprehensive investigation into deep fake detection across image, video, and audio modalities, leveraging advanced deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Through this research, we aim to address the complexities of identifying deep fake content across diverse media formats, contributing to the ongoing efforts in safeguarding digital integrity.