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Found 7 papers, 1 papers with code
On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms
However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development.
DF-Platter: Multi-Face Heterogeneous Deepfake Dataset
In this research, we emulate the real-world scenario of deepfake generation and spreading, and propose the DF-Platter dataset, which contains (i) both low-resolution and high-resolution deepfakes generated using multiple generation techniques and (ii) single-subject and multiple-subject deepfakes, with face images of Indian ethnicity.
Are Face Detection Models Biased?
Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models.
DeePhy: On Deepfake Phylogeny
In order to enable the research community to address these questions, this paper proposes DeePhy, a novel Deepfake Phylogeny dataset which consists of 5040 deepfake videos generated using three different generation techniques.
Anatomizing Bias in Facial Analysis
In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis.
Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models
We provide a systematic analysis to evaluate the performance of four state-of-the-art deep face recognition models in the presence of image distortions across different \textit{gender} and \textit{race} subgroups.
Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images
Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening.
