Detection of fake faces in videos
M. Shamanth, Russel Mathias, Dr Vijayalakshmi MN

TL;DR
This paper presents a deep learning approach using face detection and ensemble neural networks to identify fake faces in videos, achieving around 91% accuracy with focal loss optimization.
Contribution
It introduces a novel ensemble neural network model trained on video datasets with face detection, optimizing focal loss for improved fake face detection accuracy.
Findings
Focal loss improves detection accuracy and F1 score.
Achieved up to 91% accuracy in fake face detection.
Model performance varies over time due to decay.
Abstract
: Deep learning methodologies have been used to create applications that can cause threats to privacy, democracy and national security and could be used to further amplify malicious activities. One of those deep learning-powered applications in recent times is synthesized videos of famous personalities. According to Forbes, Generative Adversarial Networks(GANs) generated fake videos growing exponentially every year and the organization known as Deeptrace had estimated an increase of deepfakes by 84% from the year 2018 to 2019. They are used to generate and modify human faces, where most of the existing fake videos are of prurient non-consensual nature, of which its estimates to be around 96% and some carried out impersonating personalities for cyber crime. In this paper, available video datasets are identified and a pretrained model BlazeFace is used to detect faces, and a ResNet and…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection · Face recognition and analysis
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Depthwise Convolution · Pointwise Convolution · Batch Normalization · Average Pooling · Residual Connection · Depthwise Separable Convolution · Bottleneck Residual Block · Dense Connections · 1x1 Convolution
