DeepFakes Detection in Videos using Feature Engineering Techniques in Deep Learning Convolution Neural Network Frameworks

Abstract

In this paper, we discuss the intermediate results of our on-going study of DeepFakes detection in videos. Our core focus is in exploitation of feature engineering as a precursor filtering technique, to the deep learning-based convolution neural network (CNN) classification frameworks. In previous research, we focused on the standard deviation of the points of interest from SIFT or Scale-Invariant Feature Transform, to detect whether visual media has been compromised with misinformation. Hence, in this new approach we rely on classical frequency analysis of images that reveals different behaviors at higher frequencies. We noticed in literature review that there lies a distinct contrast of range of frequency component that emphasize important information in images [11]. We plan to use Discrete Wavelet Transform, abbreviated as DWT, and anticipate improvement of detection accuracy when used on complex and poor-quality images of FaceForensics++. The DWT features will be input to the CNN binary classification to further analyze the performance essentially when detecting this fraudulent information and to compare the detection performance against previous research which used Scale-Invariant Feature Transform.