A Machine Learning Approach for DeepFake Detection

TL;DR

This paper introduces a convolutional neural network-based method for detecting DeepFake images, achieving 95% accuracy on the Celeb-DF dataset, contributing to security and misinformation mitigation.

Contribution

It presents a novel deep learning approach using CNNs and a dedicated dataset for effective DeepFake detection, nearing state-of-the-art performance.

Findings

Achieved 95% accuracy on Celeb-DF dataset.

Model performance close to current state-of-the-art methods.

Potential for future improvements in manipulation technique detection.

Abstract

With the spread of DeepFake techniques, this technology has become quite accessible and good enough that there is concern about its malicious use. Faced with this problem, detecting forged faces is of utmost importance to ensure security and avoid socio-political problems, both on a global and private scale. This paper presents a solution for the detection of DeepFakes using convolution neural networks and a dataset developed for this purpose - Celeb-DF. The results show that, with an overall accuracy of 95% in the classification of these images, the proposed model is close to what exists in the state of the art with the possibility of adjustment for better results in the manipulation techniques that arise in the future.