Nasir Memon

About

Nasir Memon is a professor of Computer Science and Engineering at the New York University Tandon School of Engineering and serves as the Dean of Engineering at NYU Shanghai. He has been a faculty member at NYU Tandon since September 1998. His research interests encompass digital forensics, biometrics, data compression, network security, and security and human behavior. Memon introduced cybersecurity studies to NYU Tandon in 1999, making it one of the first institutions to implement undergraduate cybersecurity programs. He is a co-founder of NYU's Center for Cyber Security (CCS) at New York and NYU Abu Dhabi, and he founded the OSIRIS Lab, CSAW, the NYU Tandon Bridge program, and the Cyber Fellows program at NYU. Memon has received multiple awards for his research and teaching, including best paper awards, the Jacobs Excellence in Education Award, and recognition as an IEEE Fellow and SPIE Fellow for his contributions to image compression and media security and forensics. His professional activities include editorial board memberships and serving as the Editor-In-Chief of the IEEE Transactions on Information Security and Forensics.

Research topics

• Computer Security
• Computer Science
• Political Science
• Business
• Data science
• Internet privacy
• Risk analysis (engineering)
• World Wide Web
• Psychology
• Engineering

Selected publications

• Effects of Credibility Indicators on Social Media News Sharing Intent

  2020 · 162 citations

  In recent years, social media services have been leveraged to spread fake news stories. Helping people spot fake stories by marking them with credibility indicators could dissuade them from sharing such stories, thus reducing their amplification. We carried out an online study (N = 1,512) to explore the impact of four types of credibility indicators on people's intent to share news headlines with their friends on social media. We confirmed that credibility indicators can indeed decrease the prop…

  DOI

• True or False: Studying the Work Practices of Professional Fact-Checkers

  Proceedings of the ACM on Human-Computer Interaction · 2022 · 62 citations

  Misinformation has developed into a critical societal threat that can lead to disastrous societal consequences. Although fact-checking plays a key role in combating misinformation, relatively little research has empirically investigated work practices of professional fact-checkers. To address this gap, we conducted semi-structured interviews with 21 fact-checkers from 19 countries. The participants reported being inundated with information that needs filtering and prioritizing prior to fact-chec…

  DOI

• Identity-Preserving Aging of Face Images via Latent Diffusion Models

  2023-09-25 · 16 citations

  articleSenior author

  The performance of automated face recognition systems is inevitably impacted by the facial aging process. However, high quality datasets of individuals collected over several years are typically small in scale. In this work, we propose, train, and validate the use of latent text-to-image diffusion models for synthetically aging and de-aging face images. Our models succeed with few-shot training, and have the added benefit of being controllable via intuitive textual prompting. We observe high deg…

  PublisherDOI

• Gotcha: Real-Time Video Deepfake Detection via Challenge-Response

  2024-07-08 · 13 citations

  articleSenior author

  With the rise of AI-enabled Real-Time Deepfakes (RTDFs), the integrity of online video interactions has become a growing concern. RTDFs have now made it feasible to replace an imposter's face with their victim in live video interactions. Such advancement in deepfakes also coaxes detection to rise to the same standard. However, existing deepfake detection techniques are asynchronous and hence ill-suited for RTDFs. To bridge this gap, we propose a challenge-response approach that establishes authe…

  PublisherDOI

• Zero-Shot Racially Balanced Dataset Generation using an Existing Biased StyleGAN2

  2023-09-25 · 11 citations

  article

  Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets. Further, many of these datasets lack diversity in terms of ethnicity and demographics, which can lead to biased models that can have serious societal and security implications. To address these issues, we propose a methodology that leverages the biased generative model StyleGAN2 to create demographically diverse images of synthetic individ…

  PublisherDOI

Recent grants

• TWC: Medium: Collaborative: Towards Secure, Robust, and Usable Gesture-Based Authentication

  NSF · $400k · 2012–2016

• ASPIRE: An SFS Program for Interdisciplinary Research and Education

  NSF · $2.1M · 2009–2014

• CT-ISG Security and Privacy of Biometric Templates: Theory and Practice

  NSF · $200k · 2007–2010

Frequent coauthors

• Hüsrev Taha Sencar

  63 shared

• Ahmet Emir Dirik

  38 shared

• Bülent Sankur

  34 shared

• Sevinç Bayram

  Hitachi (United Kingdom)

  34 shared

• İsmail Avcıbaş

  Ostim Technical University

  30 shared

• Paweł Korus

  Amazon (United States)

  25 shared

• Tzipora Halevi

  Brooklyn College

  23 shared

• Khalid Sayood

  University of Nebraska–Lincoln

  23 shared

Labs

• OSIRIS LabPI

Awards & honors

• Best Paper Award: DeepMasterPrints: Generating MasterPrints…
• SPIE Fellow 2014
• Best Research in Advanced ID Systems: Online Authentication…
• Best Paper Award: Xiang Liu, Liyun Li, and Nasir Memon (2013…
• Best Paper Award: IEEE Signal Processing Society. Protecting…

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