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Error Level Analysis As A Guide Mask For Robust Deepfake Detection

為了解決Reface app的問題，作者吳財俊 這樣論述：

Recently, people tend to use many video calls for work and important meetings, making them more prone to the Deepfake attack. Deepfake uses machine learning to manipulate video, making it almost impossible to distinguish by human eyes. The criminal can use the Deepfake technology to manipulate thos

e videos and make people misunderstand that individual or even the company. So, that is why distinguishing the fake video created by Deepfake becomes very important. Many research pieces study Deepfake detection and can achieve quite good results. However, Deepfake technology is continuously evolvin

g along with the growth of AI and machine learning, making new methods of creating fake videos released one after another. As a result, there are now many novel methods to create Deepfake videos which makes previous models for detecting Deepfake become inefficient because the previous models have no

t learned about the new methods. Since there are many methods to manipulate the video, the robustness of the detecting model becomes a challenge.In this thesis, the custom dataset is used by combining many existing datasets together, such as FaceForensic++, Celeb-DF, etc. Also, a balance partition b

etween the videos that are picked from each dataset is considered in order to achieve more diversity in face identities and creation methods, which can lead to achieving high robustness when training the model. Moreover, the new model of Deepfake detection is proposed in this thesis which is the ELA

-Inception-Resnet. The ELA-InceptionResnet combines the Error Level Analysis and the Inception-Resnet architecture together by using the Error Level Analysis to guide the Inception-Resnet model on defining which features are essential for distinguishing manipulated videos since each input videos are

having different types of manipulating methods, so the distinguishing features of each method could be different. The Error Level Analysis guides the model by giving different weights to the features map, higher weights for more essential features, and vice versa. After several experiments, ELA-Inc

eptionResnet became the most robust model compared to the state-of-art model with the highest average accuracy of 91.41% among four different test datasets. However, ELA-InceptionResnet may not be able to beat the accuracy of the native model of each dataset since those models are explicitly trained

on those datasets.