# GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

arXiv: https://arxiv.org/abs/2207.10246 · HTML: https://mlsanity.com/paper/2207.10246/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [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)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [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)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [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)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)
- [How Does Gender Balance In Training Data Affect Face Recognition Accuracy?](https://mlsanity.com/paper/2002.02934/) (2020)
- [An Examination of Fairness of AI Models for Deepfake Detection](https://mlsanity.com/paper/2105.00558/) (2021)
- [On Improving Cross-dataset Generalization of Deepfake Detectors](https://mlsanity.com/paper/2204.04285/) (2022)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [Anatomizing Bias in Facial Analysis](https://mlsanity.com/paper/2112.06522/) (2021)
- [Identity-Driven DeepFake Detection](https://mlsanity.com/paper/2012.03930/) (2020)
- [An Experimental Evaluation on Deepfake Detection using Deep Face Recognition](https://mlsanity.com/paper/2110.01640/) (2021)
- [Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers](https://mlsanity.com/paper/2110.04427/) (2021)

## Built on by (most cited first, 30 of 32)

- [Preserving Fairness Generalization in Deepfake Detection](https://mlsanity.com/paper/2402.17229/) (2024)
- [Improving Fairness in Deepfake Detection](https://mlsanity.com/paper/2306.16635/) (2023)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection](https://mlsanity.com/paper/2310.02234/) (2023)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries](https://mlsanity.com/paper/2402.01721/) (2024)
- [Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations](https://mlsanity.com/paper/2407.14367/) (2024)
- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [Fair Deepfake Detectors Can Generalize](https://mlsanity.com/paper/2507.02645/) (2025)
- [Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization](https://mlsanity.com/paper/2408.01532/) (2024)
- [Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking](https://mlsanity.com/paper/2410.01906/) (2024)
- [SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis](https://mlsanity.com/paper/2508.07944/) (2025)
- [Data-Driven Fairness Generalization for Deepfake Detection](https://mlsanity.com/paper/2412.16428/) (2024)
- [Rethinking Individual Fairness in Deepfake Detection](https://mlsanity.com/paper/2507.14326/) (2025)
- [Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis](https://mlsanity.com/paper/2603.09007/) (2026)
- [Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection](https://mlsanity.com/paper/2405.15173/) (2024)
- [Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection](https://mlsanity.com/paper/2511.10150/) (2025)
- [Is Facial Recognition Biased at Near-Infrared Spectrum As Well?](https://mlsanity.com/paper/2211.00129/) (2022)
- [Facial Forgery-based Deepfake Detection using Fine-Grained Features](https://mlsanity.com/paper/2310.07028/) (2023)
- [Fair and Interpretable Deepfake Detection in Videos](https://mlsanity.com/paper/2510.17264/) (2025)
- [Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias](https://mlsanity.com/paper/2605.09087/) (2026)
- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)
- [The Folly of AI for Age Verification](https://mlsanity.com/paper/2506.00038/) (2025)
- [Bias Analysis for Synthetic Face Detection: A Case Study of the Impact of Facial Attributes](https://mlsanity.com/paper/2507.19705/) (2025)
- [Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization](https://mlsanity.com/paper/2509.07993/) (2025)
- [Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification](https://mlsanity.com/paper/2512.11015/) (2025)
- [Robust Fair Disease Diagnosis in CT Images](https://mlsanity.com/paper/2604.09710/) (2026)
- [Toward Calibrated, Fair, and accurate Deepfake Detection](https://mlsanity.com/paper/2606.09881/) (2026)
- [The Calibrated Deepfake Trust Score (CDTS): Competence-Coupled Trust Degradation Across Deepfake Detectors](https://mlsanity.com/paper/2606.29484/) (2026)

## Abstract (arXiv)

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For the commercial adoption and massive roll-out of the deepfake detection technology, it is vital to evaluate and understand the fairness (the absence of any prejudice or favoritism) of deepfake detectors across demographic variations such as gender and race. As the performance differential of deepfake detectors between demographic subgroups would impact millions of people of the deprived sub-group. This paper aims to evaluate the fairness of the deepfake detectors across males and females. However, existing deepfake datasets are not annotated with demographic labels to facilitate fairness analysis. To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender. Our analysis on the gender-labeled version of the datasets suggests (a) current deepfake datasets have skewed distribution across gender, and (b) commonly adopted deepfake detectors obtain unequal performance across gender with mostly males outperforming females. Finally, we contributed a gender-balanced and annotated deepfake dataset, GBDF, to mitigate the performance differential and to promote research and development towards fairness-aware deep fake detectors. The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF