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Seeing Isn't Believing: Addressing the Societal Impact of Deepfakes in Low-Tech Environments

Azmine Toushik Wasi, Rahatun Nesa Priti, Mahir Absar Khan, Abdur Rahman, Mst Rafia Islam

TL;DR

The paper investigates the societal impact of deepfakes in low-tech environments, focusing on the Global South, by conducting a Bangladesh-based survey to understand perceptions and harms. It proposes a three-stage Prevention–Detection–Mitigation framework that integrates regulatory, educational, and technical measures, including AI-based detection, platform accountability, and victim support. Key findings reveal knowledge gaps, limited detection confidence, and strong demand for accessible verification tools and trusted information channels. The work offers actionable, context-aware recommendations and calls for interdisciplinary, cross-border collaboration to counter deepfake threats in resource-constrained settings.

Abstract

Deepfakes, AI-generated multimedia content that mimics real media, are becoming increasingly prevalent, posing significant risks to political stability, social trust, and economic well-being, especially in developing societies with limited media literacy and technological infrastructure. This work aims to understand how these technologies are perceived and impact resource-limited communities. We conducted a survey to assess public awareness, perceptions, and experiences with deepfakes, leading to the development of a comprehensive framework for prevention, detection, and mitigation in tech-limited environments. Our findings reveal critical knowledge gaps and a lack of effective detection tools, emphasizing the need for targeted education and accessible verification solutions. This work offers actionable insights to support vulnerable populations and calls for further interdisciplinary efforts to tackle deepfake challenges globally, particularly in the Global South.

Seeing Isn't Believing: Addressing the Societal Impact of Deepfakes in Low-Tech Environments

TL;DR

The paper investigates the societal impact of deepfakes in low-tech environments, focusing on the Global South, by conducting a Bangladesh-based survey to understand perceptions and harms. It proposes a three-stage Prevention–Detection–Mitigation framework that integrates regulatory, educational, and technical measures, including AI-based detection, platform accountability, and victim support. Key findings reveal knowledge gaps, limited detection confidence, and strong demand for accessible verification tools and trusted information channels. The work offers actionable, context-aware recommendations and calls for interdisciplinary, cross-border collaboration to counter deepfake threats in resource-constrained settings.

Abstract

Deepfakes, AI-generated multimedia content that mimics real media, are becoming increasingly prevalent, posing significant risks to political stability, social trust, and economic well-being, especially in developing societies with limited media literacy and technological infrastructure. This work aims to understand how these technologies are perceived and impact resource-limited communities. We conducted a survey to assess public awareness, perceptions, and experiences with deepfakes, leading to the development of a comprehensive framework for prevention, detection, and mitigation in tech-limited environments. Our findings reveal critical knowledge gaps and a lack of effective detection tools, emphasizing the need for targeted education and accessible verification solutions. This work offers actionable insights to support vulnerable populations and calls for further interdisciplinary efforts to tackle deepfake challenges globally, particularly in the Global South.

Paper Structure

This paper contains 27 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data Collection
  4. Framework Design
  5. Understanding User Perceptions.
  6. Connecting the Dots.
  7. Framework
  8. Prevention: Preventing from Creating Deepfakes
  9. Strengthening Laws and Regulations
  10. Raising Public Awareness.
  11. Ethical AI Development.
  12. Developing AI-based Detection and Mitigation Tools.
  13. Detection: Detecting and Stopping the Spread of Deepfakes
  14. Strengthening Laws and Regulations.
  15. Raising Public Awareness.
  16. ...and 12 more sections

Figures (3)

  • Figure 1: Motivation behind this study is to understand the adverse impact of deepfakes in tech-limited environments, where limited access to digital resources and media literacy exacerbate the risks posed by AI-generated content.
  • Figure 2: Data Analysis: Statistics.
  • Figure 3: Our framework for handling deepfakes in low-tech environments consists of three key stages: prevention, detection, and mitigation. In the prevention stage, we focus on the ethical challenges and technological factors involved in the creation of deepfakes, aiming to reduce their emergence and impact.