no code implementations • 12 Mar 2024 • Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde, Nasir Memon
To mitigate this issue, we propose two novel techniques for local and global attribute editing.
no code implementations • 20 Nov 2023 • Sudipta Banerjee, Anubhav Jain, Zehua Jiang, Nasir Memon, Julian Togelius, Arun Ross
A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security.
1 code implementation • 17 Jul 2023 • Sudipta Banerjee, Govind Mittal, Ameya Joshi, Chinmay Hegde, Nasir Memon
The performance of automated face recognition systems is inevitably impacted by the facial aging process.
no code implementations • 10 Apr 2023 • Nitish Shukla, Sudipta Banerjee
Adversarial attacks in the input (pixel) space typically incorporate noise margins such as $L_1$ or $L_{\infty}$-norm to produce imperceptibly perturbed data that confound deep learning networks.
no code implementations • 7 Sep 2022 • Sudipta Banerjee, Aditi Aggarwal, Arun Ross
Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems.
no code implementations • 7 Sep 2022 • Sudipta Banerjee, Prateek Jaiswal, Arun Ross
In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process.
no code implementations • 5 Jul 2021 • Sudipta Banerjee, Arun Ross
We performed experiments on AMSL face morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of the proposed method.
no code implementations • 2 Jan 2021 • Sudipta Banerjee, Arun Ross
In this work, we propose a method to simultaneously perform (i) biometric recognition (i. e., identify the individual), and (ii) device recognition, (i. e., identify the device) from a single biometric image, say, a face image, using a one-shot schema.
no code implementations • 17 Sep 2020 • Sudipta Banerjee, Arun Ross
The principle of Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image.
no code implementations • 21 Feb 2020 • Sudipta Banerjee, Arun Ross
We also utilize the same basis functions to model geometric transformations and deep-learning based transformations.
no code implementations • 12 May 2019 • Arun Ross, Sudipta Banerjee, Cunjian Chen, Anurag Chowdhury, Vahid Mirjalili, Renu Sharma, Thomas Swearingen, Shivangi Yadav
The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service.
no code implementations • 31 Aug 2018 • Sudipta Banerjee, Vahid Mirjalili, Arun Ross
The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i. e., the sensor that produced it.