# Deepfake Detection using Spatiotemporal Convolutional Networks

arXiv: https://arxiv.org/abs/2006.14749 · HTML: https://mlsanity.com/paper/2006.14749/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 30 of 32)

- [Exploring Temporal Coherence for More General Video Face Forgery Detection](https://mlsanity.com/paper/2108.06693/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Combining EfficientNet and Vision Transformers for Video Deepfake Detection](https://mlsanity.com/paper/2107.02612/) (2021)
- [AltFreezing for More General Video Face Forgery Detection](https://mlsanity.com/paper/2307.08317/) (2023)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [Video Transformer for Deepfake Detection with Incremental Learning](https://mlsanity.com/paper/2108.05307/) (2021)
- [Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning](https://mlsanity.com/paper/2309.05911/) (2023)
- [Deepfake Detection Scheme Based on Vision Transformer and Distillation](https://mlsanity.com/paper/2104.01353/) (2021)
- [Model Attribution of Face-swap Deepfake Videos](https://mlsanity.com/paper/2202.12951/) (2022)
- [3D-Aware Video Generation](https://mlsanity.com/paper/2206.14797/) (2022)
- [FedForgery: Generalized Face Forgery Detection with Residual Federated Learning](https://mlsanity.com/paper/2210.09563/) (2022)
- [A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features](https://mlsanity.com/paper/2208.00788/) (2022)
- [SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection](https://mlsanity.com/paper/2508.09913/) (2025)
- [Deep Semantic Manipulation of Facial Videos](https://mlsanity.com/paper/2111.07902/) (2021)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Cross-Domain Local Characteristic Enhanced Deepfake Video Detection](https://mlsanity.com/paper/2211.03346/) (2022)
- [CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos](https://mlsanity.com/paper/2311.03782/) (2023)
- [A Multimodal Framework for Deepfake Detection](https://mlsanity.com/paper/2410.03487/) (2024)
- [Detecting Localized Deepfake Manipulations Using Action Unit-Guided Video Representations](https://mlsanity.com/paper/2503.22121/) (2025)
- [Leveraging Deep Learning Approaches for Deepfake Detection: A Review](https://mlsanity.com/paper/2304.01908/) (2023)
- [Reduced Spatial Dependency for More General Video-level Deepfake Detection](https://mlsanity.com/paper/2503.03270/) (2025)
- [FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes](https://mlsanity.com/paper/2506.11477/) (2025)
- [DF-Captcha: A Deepfake Captcha for Preventing Fake Calls](https://mlsanity.com/paper/2208.08524/) (2022)
- [Facial Forgery-based Deepfake Detection using Fine-Grained Features](https://mlsanity.com/paper/2310.07028/) (2023)
- [Unearthing Common Inconsistency for Generalisable Deepfake Detection](https://mlsanity.com/paper/2311.11549/) (2023)
- [Challenges and Solutions in DeepFakes](https://mlsanity.com/paper/2109.05397/) (2021)
- [Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization](https://mlsanity.com/paper/2204.06228/) (2022)
- [A Machine Learning Approach for DeepFake Detection](https://mlsanity.com/paper/2209.13792/) (2022)
- [Learning Expressive And Generalizable Motion Features For Face Forgery Detection](https://mlsanity.com/paper/2403.05172/) (2024)

## Abstract (arXiv)

Better generative models and larger datasets have led to more realistic fake videos that can fool the human eye but produce temporal and spatial artifacts that deep learning approaches can detect. Most current Deepfake detection methods only use individual video frames and therefore fail to learn from temporal information. We created a benchmark of the performance of spatiotemporal convolutional methods using the Celeb-DF dataset. Our methods outperformed state-of-the-art frame-based detection methods. Code for our paper is publicly available at https://github.com/oidelima/Deepfake-Detection.