DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

TL;DR

DeepRhythm detects DeepFake videos by analyzing disruptions in heartbeat rhythms derived from skin color changes, offering a novel and effective approach that generalizes across datasets and generation techniques.

Contribution

This paper introduces DeepRhythm, a novel DeepFake detection method that leverages heartbeat rhythm analysis using dual-spatial-temporal attention, demonstrating superior effectiveness and generalization.

Findings

Effective detection of DeepFakes via heartbeat rhythm analysis.

Robust performance across multiple datasets and DeepFake generation methods.

Generalizes well under various challenging degradations.

Abstract

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake detection technique that exposes DeepFakes by monitoring the heartbeat rhythms. DeepRhythm utilizes dual-spatial-temporal attention to adapt to dynamically changing face and fake types. Extensive experiments on FaceForensics++ and DFDC-preview datasets have confirmed our conjecture and demonstrated not only the effectiveness, but also the generalization capability of \emph{DeepRhythm} over different datasets by various DeepFakes generation techniques and multifarious challenging degradations.