DEEPFAKE DETECTION USING LSTM & RESNEXT-50

DEEPFAKE DETECTION USING LSTM & RESNEXT-50

1 MS. M. VARALAKSHMI, 2 SAKETH PAIDA, 3 L. RUTHWIK REDDY, 4 J. SAMEL

1 Assistant Professor

Department of Information Technology,

Mahatma Gandhi Institution of Technology, Gandipet, Hyderabad.

mvaralakshmi_it@mgit.ac.in

2 Department of Computer Science and Business Systems,

Mahatma Gandhi Institution of Technology, Gandipet, Hyderabad.

psaketh_csb203247@mgit.ac.in

3 Department of Computer Science and Business Systems,

Mahatma Gandhi Institution of Technology, Gandipet, Hyderabad.

lruthwikreddy_csb203232@mgit.ac.in

4 Department of Computer Science and Business Systems,

Mahatma Gandhi Institution of Technology, Gandipet, Hyderabad.

jsamel_csb203223@mgit.ac.in

Abstract:

 As computing power increases, "deep fakes," or films that seem like they were created by a real person, are becoming feasible thanks to deep learning algorithms. Political instability, terrorism, revenge porn, or extortion can be the motivation for these lifelike face swapped deep fakes. This research presents a novel deep learning system that can recognize the difference between real and AI-generated films.We have developed a method that can identify deep fake replacements and reenactments automatically. We're using AI to combat AI. Our system's Res-Next Convolution neural network retrieves properties at the frame level. A recurrent neural network (RNN) trained using long short-term memory (LSTM) characteristics can distinguish between deep films

and regular movies. On big, balanced, and mixed datasets, our technique is tested by Face-Forensic++ [1], Deepfake Detection Challenge [2], and Celeb-DF [3]. The quality of real-time data is enhanced by this. We prove that our technology consistently outperforms the competitors.
Computer vision, Res-Next Convolution neural network, RNN, and LSTM are some of the index phrases.