GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

• URL: http://arxiv.org/abs/2207.10246v1
• Date: Thu, 21 Jul 2022 01:00:40 GMT
• Title: GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
• Authors: Aakash Varma Nadimpalli and Ajita Rattani
• Abstract summary: Facial forgery by deepfakes has raised severe societal concerns. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. It is vital to evaluate and understand the fairness of deepfake detectors across demographic variations such as gender and race.
• Score: 1.0152838128195467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• On the "Illusion" of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes [7.602456562464879]
  Face recognition systems exhibit significant accuracy differences based on the user's gender. We propose a toolchain to effectively decorrelate and aggregate facial attributes to enable a less-biased gender analysis. Experiments show that the gender gap vanishes when images of male and female subjects share specific attributes.
  arXiv  Detail & Related papers  (2025-01-21T10:21:19Z)
• Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook [101.30779332427217]
  We survey deepfake generation and detection techniques, including the most recent developments in the field. We identify various kinds of deepfakes, according to the procedure used to alter or generate the fake content. We develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content.
  arXiv  Detail & Related papers  (2024-11-29T08:29:25Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods. We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results. We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• DF40: Toward Next-Generation Deepfake Detection [62.073997142001424]
  existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset and testing them on other prevalent deepfake datasets. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? We construct a highly diverse deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques.
  arXiv  Detail & Related papers  (2024-06-19T12:35:02Z)
• Preserving Fairness Generalization in Deepfake Detection [14.485069525871504]
  Deepfake detection models can result in unfair performance disparities among demographic groups, such as race and gender. We propose the first method to address the fairness generalization problem in deepfake detection by simultaneously considering features, loss, and optimization aspects. Our method employs disentanglement learning to extract demographic and domain-agnostic features, fusing them to encourage fair learning across a flattened loss landscape.
  arXiv  Detail & Related papers  (2024-02-27T05:47:33Z)
• Improving Fairness in Deepfake Detection [38.999205139257164]
  biases in the data used to train deepfake detectors can lead to disparities in detection accuracy across different races and genders. We propose novel loss functions that handle both the setting where demographic information is available as well as the case where this information is absent.
  arXiv  Detail & Related papers  (2023-06-29T02:19:49Z)
• Analyzing Fairness in Deepfake Detection With Massively Annotated Databases [9.407035514709293]
  We investigate factors causing biased detection in public Deepfake datasets. We create large-scale demographic and non-demographic annotations with 47 different attributes for five popular Deepfake datasets. We analyse attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets.
  arXiv  Detail & Related papers  (2022-08-11T14:28:21Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)
• An Examination of Fairness of AI Models for Deepfake Detection [5.4852920337961235]
  We evaluate bias present in deepfake datasets and detection models across protected subgroups. Using facial datasets balanced by race and gender, we examine three popular deepfake detectors and find large disparities in predictive performances across races.
  arXiv  Detail & Related papers  (2021-05-02T21:55:04Z)
• Balancing Biases and Preserving Privacy on Balanced Faces in the Wild [50.915684171879036]
  There are demographic biases present in current facial recognition (FR) models. We introduce our Balanced Faces in the Wild dataset to measure these biases across different ethnic and gender subgroups. We find that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. We propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks.
  arXiv  Detail & Related papers  (2021-03-16T15:05:49Z)
• WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [82.42495493102805]
  We introduce a new dataset WildDeepfake which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet. We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically.
  arXiv  Detail & Related papers  (2021-01-05T11:10:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.