DFCatcher: A Deep CNN Model to Identify Deepfake Face Images
Arpita Dhar, Likhan Biswas, Prima Acharjee, Shemonti Ahmed, Abida Sultana, Dewan Ziaul Karim, Mohammad Zavid Parvez · TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) · 2021
In recent years, advancement in the realm of machine learning has introduced a feature known as Deepfake pictures, which allows users to substitute a genuine face with a fake one that seems real. As a result, distinguishing between authentic and fraudulent pictures has become difficult. There have been several cases in recent years where Deepfake pictures have been used to defame famous leaders and even regular people. Furthermore, cases have been documented in which Deepfake yet realistic pictures were used to promote political discontent, blackmail, spread fake news, and even carry out false terrorism attacks. The objective of our model is to differentiate between real and Deepfake images so that the above mentioned situations can be avoided. This project represents a deep CNN model with 13000 images divided in two segments that are: Training and Testing. The dataset was prepared using necessary image augmentation techniques. A total of 2 categories are considered (real image category and fake image category). Our suggested model was successful in achieving 98.77% accuracy. The model shows promising results in the case of detecting real and DeepFake images than all the other models used before.