no code implementations • 14 Dec 2022 • Jiaxiang Jiang, Michael Goebel, Cezar Borba, William Smith, B. S. Manjunath
A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features.
no code implementations • 17 Nov 2022 • Ekta Prashnani, Michael Goebel, B. S. Manjunath
Overall, with PhaseForensics, we show improved distortion and adversarial robustness, and state-of-the-art cross-dataset generalization, with 91. 2% video-level AUC on the challenging CelebDFv2 (a recent state-of-the-art compares at 86. 9%).
1 code implementation • 17 Aug 2022 • Jiaxiang Jiang, Amil Khan, S. Shailja, Samuel A. Belteton, Michael Goebel, Daniel B. Szymanski, B. S. Manjunath
This paper presents a method for time-lapse 3D cell analysis.
no code implementations • 12 Apr 2021 • Lakshmanan Nataraj, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath
While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations.
1 code implementation • 19 Mar 2021 • Michael Goebel, Jason Bunk, Srinjoy Chattopadhyay, Lakshmanan Nataraj, Shivkumar Chandrasekaran, B. S. Manjunath
Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment.
1 code implementation • 18 Nov 2020 • Satish Kumar, A S M Iftekhar, Michael Goebel, Tom Bullock, Mary H. MacLean, Michael B. Miller, Tyler Santander, Barry Giesbrecht, Scott T. Grafton, B. S. Manjunath
Precise measurement of physiological signals is critical for the effective monitoring of human vital signs.
no code implementations • 20 Jul 2020 • Michael Goebel, Lakshmanan Nataraj, Tejaswi Nanjundaswamy, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath
Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers.
1 code implementation • 16 Apr 2019 • Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents.