# DeepFake Detection in Dyadic Video Calls using Point of Gaze Tracking

arXiv: https://arxiv.org/abs/2509.25503 · HTML: https://mlsanity.com/paper/2509.25503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues](https://mlsanity.com/paper/2003.06711/) (2020)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [End-to-end speaker segmentation for overlap-aware resegmentation](https://mlsanity.com/paper/2104.04045/) (2021)
- [Rendering of Eyes for Eye-Shape Registration and Gaze Estimation](https://mlsanity.com/paper/1505.05916/) (2015)
- [Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking](https://mlsanity.com/paper/2101.01165/) (2021)
- [GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response](https://mlsanity.com/paper/2210.06186/) (2022)
- [GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning](https://mlsanity.com/paper/2311.07075/) (2023)

## Built on by (most cited first, 1 of 1)

- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)

## Abstract (arXiv)

With recent advancements in deepfake technology, it is now possible to generate convincing deepfakes in real-time. Unfortunately, malicious actors have started to use this new technology to perform real-time phishing attacks during video meetings. The nature of a video call allows access to what the deepfake is ``seeing,'' that is, the screen displayed to the malicious actor. Using this with the estimated gaze from the malicious actors streamed video enables us to estimate where the deepfake is looking on screen, the point of gaze. Because the point of gaze during conversations is not random and is instead used as a subtle nonverbal communicator, it can be used to detect deepfakes, which are not capable of mimicking this subtle nonverbal communication. This paper proposes a real-time deepfake detection method adapted to this genre of attack, utilizing previously unavailable biometric information. We built our model based on explainable features selected after careful review of research on gaze patterns during dyadic conversations. We then test our model on a novel dataset of our creation, achieving an accuracy of 82%. This is the first reported method to utilize point-of-gaze tracking for deepfake detection.