# Do Deepfake Detectors Work in Reality?

arXiv: https://arxiv.org/abs/2502.10920 · HTML: https://mlsanity.com/paper/2502.10920/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [Towards Robust Blind Face Restoration with Codebook Lookup Transformer](https://mlsanity.com/paper/2206.11253/) (2022)
- [Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection](https://mlsanity.com/paper/2203.12208/) (2022)
- [DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection](https://mlsanity.com/paper/2307.01426/) (2023)
- [The DeepFake Detection Challenge (DFDC) Dataset](https://mlsanity.com/paper/2006.07397/) (2020)
- [LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection](https://mlsanity.com/paper/2401.13856/) (2024)
- [A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials](https://mlsanity.com/paper/2205.05467/) (2022)
- [Segment anything, from space?](https://mlsanity.com/paper/2304.13000/) (2023)
- [Benchmarking deep inverse models over time, and the neural-adjoint method](https://mlsanity.com/paper/2009.12919/) (2020)
- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022)

## Built on by (most cited first, 11 of 11)

- [How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study](https://mlsanity.com/paper/2602.07814/) (2026)
- [Can Multi-modal (reasoning) LLMs work as deepfake detectors?](https://mlsanity.com/paper/2503.20084/) (2025)
- [GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment](https://mlsanity.com/paper/2604.25370/) (2026)
- [Out of the box age estimation through facial imagery: A Comprehensive Benchmark of Vision-Language Models vs. out-of-the-box Traditional Architectures](https://mlsanity.com/paper/2602.07815/) (2026)
- [AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents](https://mlsanity.com/paper/2602.20569/) (2026)
- [DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis](https://mlsanity.com/paper/2603.01433/) (2026)
- [GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics](https://mlsanity.com/paper/2603.11442/) (2026)
- [Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection](https://mlsanity.com/paper/2509.14120/) (2025)
- [Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems](https://mlsanity.com/paper/2602.19539/) (2026)
- [A Synthetic Eye Movement Dataset for Script Reading Detection: Real Trajectory Replay on a 3D Simulator](https://mlsanity.com/paper/2604.05475/) (2026)
- [ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation](https://mlsanity.com/paper/2609.15100/) (2026)

## Abstract (arXiv)

Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing realism and potential for misuse. Despite rapid advancements in generative models, detection methods have not kept pace, creating a critical gap in defense strategies. This disparity is further amplified by the disconnect between academic research and real-world applications, which often prioritize different objectives and evaluation criteria. In this study, we take a pivotal step toward bridging this gap by presenting a novel observation: the post-processing step of super-resolution, commonly employed in real-world scenarios, substantially undermines the effectiveness of existing deepfake detection methods. To substantiate this claim, we introduce and publish the first real-world faceswap dataset, collected from popular online faceswap platforms. We then qualitatively evaluate the performance of state-of-the-art deepfake detectors on real-world deepfakes, revealing that their accuracy approaches the level of random guessing. Furthermore, we quantitatively demonstrate the significant performance degradation caused by common post-processing techniques. By addressing this overlooked challenge, our study underscores a critical avenue for enhancing the robustness and practical applicability of deepfake detection methods in real-world settings.