# DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation

arXiv: https://arxiv.org/abs/2010.00400 · HTML: https://mlsanity.com/paper/2010.00400/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [KoDF: A Large-scale Korean DeepFake Detection Dataset](https://mlsanity.com/paper/2103.10094/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [Generalizable Deepfake Detection with Phase-Based Motion Analysis](https://mlsanity.com/paper/2211.09363/) (2022)
- [Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation](https://mlsanity.com/paper/2404.09378/) (2024)
- [RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection](https://mlsanity.com/paper/2307.10642/) (2023)
- [FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training](https://mlsanity.com/paper/2307.14593/) (2023)
- [Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media](https://mlsanity.com/paper/2404.15601/) (2024)
- [Discussion Paper: The Threat of Real Time Deepfakes](https://mlsanity.com/paper/2306.02487/) (2023)
- [Digital and Physical-World Attacks on Remote Pulse Detection](https://mlsanity.com/paper/2110.11525/) (2021)
- [Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes](https://mlsanity.com/paper/2409.10889/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection](https://mlsanity.com/paper/2606.04098/) (2026)
- [Deepfake Representation with Multilinear Regression](https://mlsanity.com/paper/2108.06702/) (2021)
- [Study of detecting behavioral signatures within DeepFake videos](https://mlsanity.com/paper/2208.03561/) (2022)
- [PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics](https://mlsanity.com/paper/2310.02140/) (2023)
- [Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation](https://mlsanity.com/paper/2505.11110/) (2025)
- [Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising](https://mlsanity.com/paper/2507.08912/) (2025)
- [UMCL: Unimodal-generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection](https://mlsanity.com/paper/2511.18983/) (2025)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [Deepfake Detection using Biological Features: A Survey](https://mlsanity.com/paper/2301.05819/) (2023)
- [CareFall: Automatic Fall Detection through Wearable Devices and AI Methods](https://mlsanity.com/paper/2307.05275/) (2023)
- [Towards Generalizable Deepfake Detection with Spatial-Frequency Collaborative Learning and Hierarchical Cross-Modal Fusion](https://mlsanity.com/paper/2504.17223/) (2025)
- [Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection](https://mlsanity.com/paper/2512.04175/) (2025)
- [Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection](https://mlsanity.com/paper/2607.21776/) (2026)
- [Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos](https://mlsanity.com/paper/2609.12668/) (2026)

## Abstract (arXiv)

This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. In this work we investigate to what extent rPPG is useful for the detection of DeepFake videos. The proposed fake detector named DeepFakesON-Phys uses a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. This detection approach has been experimentally evaluated using the latest public databases in the field: Celeb-DF and DFDC. The results achieved, above 98% AUC (Area Under the Curve) on both databases, outperform the state of the art and prove the success of fake detectors based on physiological measurement to detect the latest DeepFake videos.