AI Deepfake Detection: Ability to Identify Deepfake Videos Across Generational Age Groups in Pasay City
Document Types
Paper Presentation
Research Theme (for Paper Presentation and Poster Presentation submissions only)
Computer and Software Technology, and Robotics (CSR)
School Name
National University - MOA
Track or Strand
Science, Technology, Engineering, and Mathematics (STEM)
Research Advisor (Last Name, First Name, Middle Initial)
Balili, Jun P. & Goboy, Joash J.
Start Date
25-6-2026 10:30 AM
End Date
25-6-2026 12:00 PM
Zoom Link/ Room Assignment
Online - https://zoom.us/j/91936856247?pwd=oCMfMsh44I2wb0dYsEgoInDJy59bOq.1 Meeting ID: 919 3685 6247 | Passcode: research
Abstract/Executive Summary
With the rapid advancement of artificial intelligence and the increasing spread of manipulated media that can be misused, identifying deepfakes has become a major concern as manipulated content grows more realistic, highlighting the importance of distinguishing real from manipulated media. The study examines the differences in the deepfake detection ability across the four different age groups: Generation Z, Millennials, Generation X, and Baby Boomers who live in Pasay City. The study aims to determine which age groups exhibit the highest and lowest accuracy in identifying deepfake content. The study will use a causal-comparative research design. The participants were selected through purposive sampling, and they were asked to complete an online test consisting of videos, in which each item included a real and a manipulated video. The research instrument was researcher-made but adapted from existing studies that addressed deepfake detection. The data were collected through an online test and analyzed using statistical methods, specifically ANOVA, to compare results across age groups. The findings revealed a significant difference in detection ability across age groups, with younger participants achieving much higher efficiency in identifying deepfake content than older groups. These results show that familiarity with digital technology affects an individual's ability to detect manipulated media. The study concludes that improving digital literacy across all age groups is essential to ensure we can confidently handle deepfakes and misinformation.
Keywords
deepfake videos, deepfake detection, ability to identify AI, age generations
Initial Consent for Publication
yes
Statement of Originality
yes
AI Deepfake Detection: Ability to Identify Deepfake Videos Across Generational Age Groups in Pasay City
With the rapid advancement of artificial intelligence and the increasing spread of manipulated media that can be misused, identifying deepfakes has become a major concern as manipulated content grows more realistic, highlighting the importance of distinguishing real from manipulated media. The study examines the differences in the deepfake detection ability across the four different age groups: Generation Z, Millennials, Generation X, and Baby Boomers who live in Pasay City. The study aims to determine which age groups exhibit the highest and lowest accuracy in identifying deepfake content. The study will use a causal-comparative research design. The participants were selected through purposive sampling, and they were asked to complete an online test consisting of videos, in which each item included a real and a manipulated video. The research instrument was researcher-made but adapted from existing studies that addressed deepfake detection. The data were collected through an online test and analyzed using statistical methods, specifically ANOVA, to compare results across age groups. The findings revealed a significant difference in detection ability across age groups, with younger participants achieving much higher efficiency in identifying deepfake content than older groups. These results show that familiarity with digital technology affects an individual's ability to detect manipulated media. The study concludes that improving digital literacy across all age groups is essential to ensure we can confidently handle deepfakes and misinformation.
https://animorepository.dlsu.edu.ph/conf_shsrescon/2026/BoA_CSR/2