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20242026
most citedAnalyzing the AI Nudification Application Ecosystem

3 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2026

Devilray: A Systematic Adversarial Model Revealing Blind Spots in Fake Base Station Detection

Taekkyung Oh, Duckwoo Kim, Hansung Bae +8

Fake Base Station (FBS) detection has been a critical focus of cellular security research for over two decades. However, significant financial and regulatory barriers to accessing…

cs.CR2026

Read This Paper to Get $50 Million:* An Analysis of Mobile Messaging Scams Using Reddit Data

Allison Lu, Bernardo B. P. Medeiros, Kevin R. B. Butler +1

Mobile messaging scams--fraudulent messages delivered over SMS and other mobile applications--have become a persistent and evolving security threat, yet the attributes underlying t…

cs.SD2025

Pitch Imperfect: Detecting Audio Deepfakes Through Acoustic Prosodic Analysis

Kevin Warren, Daniel Olszewski, Seth Layton +3

Audio deepfakes are increasingly in-differentiable from organic speech, often fooling both authentication systems and human listeners. While many techniques use low-level audio fea…

cs.HC20243 cited

Analyzing the AI Nudification Application Ecosystem

Cassidy Gibson, Daniel Olszewski, Natalie Grace Brigham +5

Given a source image of a clothed person (an image subject), AI-based nudification applications can produce nude (undressed) images of that person. Moreover, not only do such appli…

cs.SD20241 cited

Every Breath You Don't Take: Deepfake Speech Detection Using Breath

Seth Layton, Thiago De Andrade, Daniel Olszewski +3

Deepfake speech represents a real and growing threat to systems and society. Many detectors have been created to aid in defense against speech deepfakes. While these detectors impl…