# How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals

arXiv: https://arxiv.org/abs/2008.11363 · HTML: https://mlsanity.com/paper/2008.11363/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

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

- [Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics](https://mlsanity.com/paper/2403.14077/) (2024)
- [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)
- [A Review of Deep Learning-based Approaches for Deepfake Content Detection](https://mlsanity.com/paper/2202.06095/) (2022)
- [Generalizable Deepfake Detection with Phase-Based Motion Analysis](https://mlsanity.com/paper/2211.09363/) (2022)
- [Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking](https://mlsanity.com/paper/2101.01165/) (2021)
- [DeePhy: On Deepfake Phylogeny](https://mlsanity.com/paper/2209.09111/) (2022)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations](https://mlsanity.com/paper/2111.07601/) (2021)
- [Face Forgery Detection Based on Facial Region Displacement Trajectory Series](https://mlsanity.com/paper/2212.03678/) (2022)
- [Digital and Physical-World Attacks on Remote Pulse Detection](https://mlsanity.com/paper/2110.11525/) (2021)
- [Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals](https://mlsanity.com/paper/2110.15561/) (2021)
- [Identifying Rhythmic Patterns for Face Forgery Detection and Categorization](https://mlsanity.com/paper/2207.01199/) (2022)
- [Introducing Explicit Gaze Constraints to Face Swapping](https://mlsanity.com/paper/2305.16138/) (2023)
- [Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors](https://mlsanity.com/paper/2311.10788/) (2023)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss terms](https://mlsanity.com/paper/2402.03188/) (2024)
- [The Tug-of-War Between Deepfake Generation and Detection](https://mlsanity.com/paper/2407.06174/) (2024)
- [Detecting Deepfake Talking Heads from Facial Biometric Anomalies](https://mlsanity.com/paper/2507.08917/) (2025)
- [Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem](https://mlsanity.com/paper/2509.17550/) (2025)
- [Editing Physiological Signals in Videos Using Latent Representations](https://mlsanity.com/paper/2509.25348/) (2025)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks](https://mlsanity.com/paper/2208.06990/) (2022)
- [Deepfake Detection using Biological Features: A Survey](https://mlsanity.com/paper/2301.05819/) (2023)
- [FRRffusion: Unveiling Authenticity with Diffusion-Based Face Retouching Reversal](https://mlsanity.com/paper/2405.07582/) (2024)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)
- [Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection](https://mlsanity.com/paper/2512.04175/) (2025)
- [Interpretable facial dynamics as behavioral and perceptual traces of deepfakes](https://mlsanity.com/paper/2604.21760/) (2026)

## Abstract (arXiv)

Fake portrait video generation techniques have been posing a new threat to the society with photorealistic deep fakes for political propaganda, celebrity imitation, forged evidences, and other identity related manipulations. Following these generation techniques, some detection approaches have also been proved useful due to their high classification accuracy. Nevertheless, almost no effort was spent to track down the source of deep fakes. We propose an approach not only to separate deep fakes from real videos, but also to discover the specific generative model behind a deep fake. Some pure deep learning based approaches try to classify deep fakes using CNNs where they actually learn the residuals of the generator. We believe that these residuals contain more information and we can reveal these manipulation artifacts by disentangling them with biological signals. Our key observation yields that the spatiotemporal patterns in biological signals can be conceived as a representative projection of residuals. To justify this observation, we extract PPG cells from real and fake videos and feed these to a state-of-the-art classification network for detecting the generative model per video. Our results indicate that our approach can detect fake videos with 97.29% accuracy, and the source model with 93.39% accuracy.