GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
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Existing unimodal deepfake detectors are primarily designed to detect a single type of manipulation, such as visual, acoustic, and text.
“Existing unimodal deepfake detectors are primarily designed to detect a single type of manipulation, such as visual, acoustic, and text.”
From Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization (2024) · cites this paper as [39]
A key reason for the disparity in detection accuracy across demographic groups is that how often different demographic groups appear in the training data is imbalanced.
“A key reason for this disparity is that how often different demographic groups appear in the training data is imbalanced [16].”
From Improving Fairness in Deepfake Detection (2024) · cites this paper as [16]
The work in [16] showed significant bias in both datasets and detection models and tried to reduce performance bias across genders by providing a gender-balanced dataset.
“The work in [16] showed significant bias in both datasets and detection models and they tried to reduce the performance bias across genders by providing a gender-balanced dataset.”
From Improving Fairness in Deepfake Detection (2024) · cites this paper as [16]
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Improving Fairness in Deepfake Detection
(2024) · doi:10.1109/wacv57701.2024.00459 · Cited by 64 · see citing works
Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization
Katamneni, V. S., & Rattani, A. (2024) · 2024 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744480 · Cited by 20 · see citing works
Self-citation
- Authors
- Nadimpalli, A. V., & Rattani, A.
- Affiliations
- Wichita State University (US)
- Corresponding author
- Rattani, Ajita
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 320-337
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4385466340
- Cited by
- 46
- References
- 29
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 16 · 2023: 14 · 2022: 1
- FWCI
- 4.82
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Holmes Museum Of Anthropology (Wichita State University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, gender equity, data annotation, demographic bias, misinformation
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