# Exposing Deepfake Face Forgeries with Guided Residuals

arXiv: https://arxiv.org/abs/2205.00753 · HTML: https://mlsanity.com/paper/2205.00753/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 69 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [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)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://mlsanity.com/paper/1703.10717/) (2017)

## Built on by (most cited first, 17 of 17)

- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks](https://mlsanity.com/paper/2407.20836/) (2024)
- [DIP: Diffusion Learning of Inconsistency Pattern for General DeepFake Detection](https://mlsanity.com/paper/2410.23663/) (2024)
- [Model Synthesis for Zero-Shot Model Attribution](https://mlsanity.com/paper/2307.15977/) (2023)
- [COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection](https://mlsanity.com/paper/2308.01520/) (2023)
- [Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics](https://mlsanity.com/paper/2508.17247/) (2025)
- [Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection](https://mlsanity.com/paper/2405.15173/) (2024)
- [Unmasking Puppeteers: Leveraging Biometric Leakage to Expose Impersonation in AI-Based Videoconferencing](https://mlsanity.com/paper/2510.03548/) (2025)
- [All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark](https://mlsanity.com/paper/2602.23523/) (2026)
- [Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer](https://mlsanity.com/paper/2309.11092/) (2023)
- [Exploring Depth Information for Detecting Manipulated Face Videos](https://mlsanity.com/paper/2411.18572/) (2024)
- [Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation](https://mlsanity.com/paper/2412.01622/) (2024)
- [Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection](https://mlsanity.com/paper/2309.04795/) (2023)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)
- [Active Fake: DeepFake Camouflage](https://mlsanity.com/paper/2409.03200/) (2024)
- [TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection](https://mlsanity.com/paper/2409.07701/) (2024)
- [Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection](https://mlsanity.com/paper/2508.10741/) (2025)

## Abstract (arXiv)

Residual-domain feature is very useful for Deepfake detection because it suppresses irrelevant content features and preserves key manipulation traces. However, inappropriate residual prediction will bring side effects on detection accuracy. In addition, residual-domain features are easily affected by image operations such as compression. Most existing works exploit either spatial-domain features or residual-domain features, while neglecting that two types of features are mutually correlated. In this paper, we propose a guided residuals network, namely GRnet, which fuses spatial-domain and residual-domain features in a mutually reinforcing way, to expose face images generated by Deepfake. Different from existing prediction based residual extraction methods, we propose a manipulation trace extractor (MTE) to directly remove the content features and preserve manipulation traces. MTE is a fine-grained method that can avoid the potential bias caused by inappropriate prediction. Moreover, an attention fusion mechanism (AFM) is designed to selectively emphasize feature channel maps and adaptively allocate the weights for two streams. The experimental results show that the proposed GRnet achieves better performances than the state-of-the-art works on four public fake face datasets including HFF, FaceForensics++, DFDC and Celeb-DF. Especially, GRnet achieves an average accuracy of 97.72% on the HFF dataset, which is at least 5.25% higher than the existing works.