Publications

Demographic Bias Mitigation at Test-Time Using Uncertainty Estimation and Human-Machine Partnership

Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender …

Anoop Krishnan, Ajita Rattani

Demographic Bias Mitigation at Test-Time Using Uncertainty Estimation and Human-Machine Partnership

Demographic Fairness and Accountability of Audio and Video-based Unimodal and Bi-modal Deepfake Detectors

With the advances in deep generative models, facial forgery by advanced deepfake generation techniques has posed a severe societal and …

Vinaya Sree Katamneni, Aakash Varma Nadimpalli, Ajita Rattani

Leveraging Diffusion and Flow Matching Models for Demographic Bias Mitigation of Facial Attribute Classifiers

Published research highlights the presence of demographic bias in automated facial attribute classification algorithms, notably …

Sreeraj Ramachandran, Ajita Rattani

Leveraging Diffusion and Flow Matching Models for Demographic Bias Mitigation of Facial Attribute Classifiers

ProActive DeepFake Detection using GAN-based Visible Watermarking

With the advances in generative adversarial networks (GAN), facial manipulations called DeepFakes have caused major security risks and …

Aakash Varma Nadimpalli, Ajita Rattani

A novel approach for bias mitigation of gender classification algorithms using consistency regularization

Published research has confirmed the bias of automated face-based gender classification algorithms across gender-racial groups. …

Anoop Krishnan, Ajita Rattani

A novel approach for bias mitigation of gender classification algorithms using consistency regularization

GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to …

Aakash Varma Nadimpalli, Ajita Rattani

An Examination and Comparison of Fairness of Face and Ocular Recognition Across Gender at NIR Spectrum

Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. …

Sreeraj Ramachandran, Ajita Rattani

An Examination and Comparison of Fairness of Face and Ocular Recognition Across Gender at NIR Spectrum

Is Facial Recognition Biased at Near-Infrared Spectrum as Well?

Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal …

Anoop Krishnan, Brian Neas, Ajita Rattani

Is Facial Recognition Biased at Near-Infrared Spectrum as Well?

Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups

Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. …

Sreeraj Ramachandran, Ajita Rattani

Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups

Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

A number of studies suggest bias of the face biometrics, i.e., face recognition and soft-biometric estimation methods, across gender, …

Anoop Krishnan, Ali Almadan, Ajita Rattani

Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults