# Why Do Facial Deepfake Detectors Fail?

arXiv: https://arxiv.org/abs/2302.13156 · HTML: https://mlsanity.com/paper/2302.13156/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://mlsanity.com/paper/1912.13457/) (2019)
- [PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition](https://mlsanity.com/paper/2203.14325/) (2022)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)
- [ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images](https://mlsanity.com/paper/2112.03553/) (2021)
- [FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning](https://mlsanity.com/paper/2105.13617/) (2021)
- [CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation](https://mlsanity.com/paper/2107.02408/) (2021)
- [One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework](https://mlsanity.com/paper/2105.00187/) (2021)
- [Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era](https://mlsanity.com/paper/2202.10673/) (2022)
- [TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning](https://mlsanity.com/paper/2105.06117/) (2021)
- [Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images](https://mlsanity.com/paper/2112.08050/) (2021)
- [DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images](https://mlsanity.com/paper/2112.12001/) (2021)

## Built on by (most cited first, 12 of 12)

- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [Disrupting Diffusion-based Inpainters with Semantic Digression](https://mlsanity.com/paper/2407.10277/) (2024)
- [UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints](https://mlsanity.com/paper/2409.07913/) (2024)
- [SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection](https://mlsanity.com/paper/2509.22070/) (2025)
- [LLMs Are Not Yet Ready for Deepfake Image Detection](https://mlsanity.com/paper/2506.10474/) (2025)
- [Evaluating Deepfake Detectors in the Wild](https://mlsanity.com/paper/2507.21905/) (2025)
- [FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection](https://mlsanity.com/paper/2509.16602/) (2025)
- [Visual Language Models as Zero-Shot Deepfake Detectors](https://mlsanity.com/paper/2507.22469/) (2025)
- [From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Users](https://mlsanity.com/paper/2508.07596/) (2025)
- [Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection](https://mlsanity.com/paper/2509.23289/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [A Multi-Level Strategy for Deepfake Content Moderation under EU Regulation](https://mlsanity.com/paper/2507.08879/) (2025)

## Abstract (arXiv)

Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.