AHFE Open AccessUnderstanding Deepfakes: A Comprehensive Analysis of Creation, Generation, and Detection
Abstract
This paper provides a comprehensive analysis of deepfakes, focusing on their creation, generation, and detection. Deepfakes are realistic fabricated videos, images, or audios generated using artificial intelligence algorithms. While initially seen as a source of entertainment and commercial applications, the negative social consequences of deepfakes have become apparent. They are misused for creating adult content, blackmailing individuals, and spreading misinformation, leading to a decline in trust and potential societal implications. The paper also discusses the importance of legislation in regulating the use of deepfakes and explores techniques for their detection, including machine learning and natural language processing. Understanding deepfakes is essential to address their ethical and legal implications in today's digital landscape.
Keywords: deepfakes, artificial intelligence, autoencoders, deep neural networks, detection algorithms
DOI: 10.54941/ahfe1003290
Cite this paper
More from this volume
- A machine learning approach for optimizing waiting times in a hand surgery operation center
- Automated Decision Support for Collaborative, Interactive Classification
- Dynamically monitoring crowd-worker's reliability with interval-valued labels
- Perceptions, attitudes and trust toward artificial intelligence — An assessment of the public opinion
- Artificial Empathy: Exploring the Intersection of Digital Art and Emotional Responses to the COVID-19 Pandemic
- Machine Reading Comprehension and Expert System technologies for social innovation in the drug excipient selection process
- Image Caption Generation of Arts: Review and Outlook
- Automated Visual Story Synthesis with Character Trait Control
- Towards a Proper Evaluation of Automated Conversational Systems
- Does Imageable Language Make Your Tweets More Persuasive?
- Emotional Analysis of Candidates During Online Interviews
- Emotion Recognition from Speech via the Use of Different Audio Features, Machine Learning and Deep Learning Algorithms