Seeing Isn't Believing: Addressing the Societal Impact of Deepfakes in Low-Tech Environments
August 13, 2025 Β· Declared Dead Β· π Proceedings of the 2nd International Workshop on Diffusion of Harmful Content on Online Web
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Azmine Toushik Wasi, Rahatun Nesa Priti, Mahir Absar Khan, Abdur Rahman, Mst Rafia Islam
arXiv ID
2508.16618
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.MM,
stat.AP
Citations
0
Venue
Proceedings of the 2nd International Workshop on Diffusion of Harmful Content on Online Web
Last Checked
5 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted