# Face Deepfakes -- A Comprehensive Review

arXiv: https://arxiv.org/abs/2502.09812 · HTML: https://mlsanity.com/paper/2502.09812/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark](https://mlsanity.com/paper/2602.23523/) (2026)
- [DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning](https://mlsanity.com/paper/2511.04949/) (2025)
- [Face-Trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators](https://mlsanity.com/paper/2607.07545/) (2026)

## Abstract (arXiv)

In recent years, remarkable advancements in deep-fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.