# Deep Learning for Deepfakes Creation and Detection: A Survey

arXiv: https://arxiv.org/abs/1909.11573 · HTML: https://mlsanity.com/paper/1909.11573/

## Key facts

- Cited by 102 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 30 of 102)

- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
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- [GENIE: Higher-Order Denoising Diffusion Solvers](https://mlsanity.com/paper/2210.05475/) (2022)
- [Differentially Private Diffusion Models](https://mlsanity.com/paper/2210.09929/) (2022)
- [Align Your Steps: Optimizing Sampling Schedules in Diffusion Models](https://mlsanity.com/paper/2404.14507/) (2024)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [Intermediate Layer Optimization for Inverse Problems using Deep Generative Models](https://mlsanity.com/paper/2102.07364/) (2021)
- [CCVS: Context-aware Controllable Video Synthesis](https://mlsanity.com/paper/2107.08037/) (2021)
- [Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training](https://mlsanity.com/paper/2111.01118/) (2021)
- [IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization](https://mlsanity.com/paper/2406.10580/) (2024)
- [StyleSwap: Style-Based Generator Empowers Robust Face Swapping](https://mlsanity.com/paper/2209.13514/) (2022)
- [Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes](https://mlsanity.com/paper/2304.06470/) (2023)
- [GAN-generated Faces Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2202.07145/) (2022)
- [Face Generation and Editing with StyleGAN: A Survey](https://mlsanity.com/paper/2212.09102/) (2022)
- [Weakly-supervised deepfake localization in diffusion-generated images](https://mlsanity.com/paper/2311.04584/) (2023)
- [Deepfake Detection Scheme Based on Vision Transformer and Distillation](https://mlsanity.com/paper/2104.01353/) (2021)
- [BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis](https://mlsanity.com/paper/2311.05521/) (2023)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [DeCLIP: Decoding CLIP representations for deepfake localization](https://mlsanity.com/paper/2409.08849/) (2024)
- [VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs](https://mlsanity.com/paper/2303.15893/) (2023)
- [Exposing Lip-syncing Deepfakes from Mouth Inconsistencies](https://mlsanity.com/paper/2401.10113/) (2024)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [A Review of Deep Learning-based Approaches for Deepfake Content Detection](https://mlsanity.com/paper/2202.06095/) (2022)
- [3D-aware Blending with Generative NeRFs](https://mlsanity.com/paper/2302.06608/) (2023)
- [GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer](https://mlsanity.com/paper/2307.07036/) (2023)
- [DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization](https://mlsanity.com/paper/2411.10193/) (2024)

## Abstract (arXiv)

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.