Detecting Deep-Fake Videos from Appearance and Behavior

by   Shruti Agarwal, et al.

Synthetically-generated audios and videos – so-called deep fakes – continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel dis-information campaigns, and create non-consensual pornography. We describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. We show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.


Study of detecting behavioral signatures within DeepFake videos

There is strong interest in the generation of synthetic video imagery of...

User Behavior Assessment Towards Biometric Facial Recognition System: A SEM-Neural Network Approach

A smart home is grounded on the sensors that endure automation, safety, ...

Risk Assessment in the Face-based Watchlist Screening in e-Border

This paper concerns with facial-based watchlist technology as a componen...

AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements

Biometric systems based on Machine learning and Deep learning are being ...

Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion

In today's era of digital misinformation, we are increasingly faced with...

Protecting President Zelenskyy against Deep Fakes

The 2022 Russian invasion of Ukraine is being fought on two fronts: a br...

Please sign up or login with your details

Forgot password? Click here to reset