A Note on Deepfake Detection with Low-Resources

Authors: Piotr Kawa, Piotr Syga

Published: 2020-06-09 11:07:08+00:00

AI Summary

This paper proposes two methods for low-resource deepfake detection. First, it improves the MesoNet architecture by replacing activation functions, achieving a 1% accuracy improvement and increased consistency. Second, it introduces a new activation function, 'Pish', and explores deepfake detection using Local Feature Descriptors (LFD), enabling faster, GPU-less detection.

Abstract

Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks. Even though the technology gained its popularity as a carrier of jokes and parodies it raises a serious threat to ones security - via biometric impersonation or besmearing. In this paper we present two methods that allow detecting Deepfakes for a user without significant computational power. In particular, we enhance MesoNet by replacing the original activation functions allowing a nearly 1% improvement as well as increasing the consistency of the results. Moreover, we introduced and verified a new activation function - Pish that at the cost of slight time overhead allows even higher consistency. Additionally, we present a preliminary results of Deepfake detection method based on Local Feature Descriptors (LFD), that allows setting up the system even faster and without resorting to GPU computation. Our method achieved Equal Error Rate of 0.28, with both accuracy and recall exceeding 0.7.