# DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

arXiv: https://arxiv.org/abs/2006.07634 · HTML: https://mlsanity.com/paper/2006.07634/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 30 of 65)

- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [Spatiotemporal Inconsistency Learning for DeepFake Video Detection](https://mlsanity.com/paper/2109.01860/) (2021)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection](https://mlsanity.com/paper/2304.12489/) (2023)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Evading DeepFake Detectors via Adversarial Statistical Consistency](https://mlsanity.com/paper/2304.11670/) (2023)
- [Exploring Disentangled Content Information for Face Forgery Detection](https://mlsanity.com/paper/2207.09202/) (2022)
- [AVA: Adversarial Vignetting Attack against Visual Recognition](https://mlsanity.com/paper/2105.05558/) (2021)
- [EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer](https://mlsanity.com/paper/2312.04152/) (2023)
- [DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection](https://mlsanity.com/paper/2306.00863/) (2023)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Adversarial Relighting Against Face Recognition](https://mlsanity.com/paper/2108.07920/) (2021)
- [Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion](https://mlsanity.com/paper/2112.10936/) (2021)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [Analyzing Fairness in Deepfake Detection With Massively Annotated Databases](https://mlsanity.com/paper/2208.05845/) (2022)
- [Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge](https://mlsanity.com/paper/2403.06947/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Generative AI Literacy: Twelve Defining Competencies](https://mlsanity.com/paper/2412.12107/) (2024)
- [A Principled Design of Image Representation: Towards Forensic Tasks](https://mlsanity.com/paper/2203.00913/) (2022)
- [Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis](https://mlsanity.com/paper/2207.13064/) (2022)
- [Hybrid Transformer Network for Deepfake Detection](https://mlsanity.com/paper/2208.05820/) (2022)
- [Multimodal Graph Learning for Deepfake Detection](https://mlsanity.com/paper/2209.05419/) (2022)
- [Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection](https://mlsanity.com/paper/2303.18174/) (2023)

## Abstract (arXiv)

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 DeepRhythm over different datasets by various DeepFakes generation techniques and multifarious challenging degradations.