A Systematic Review on Deep Fake Image Generation, Detection Techniques, Ethical Implications, and Overcoming Challenges
Abstract
Deepfake technology, rooted in sophisticated machine learning techniques, utilizes deep neural networks to create highly realistic fake content such as videos, audio recordings, and images. This technology has rapidly evolved due to advancements in deep learning models, computational power, and data availability. The ethical implications, social impact, misuse, and legal frameworks surrounding Deepfake technology have been extensively studied. Detection techniques using deep learning approaches have been developed to combat the challenges posed by Deepfake content. Recommendations for future research include enhancing detection techniques, integrating explainable AI, and exploring real-time detection systems. Industry and policy implications emphasize the need for robust detection technologies, comprehensive legal frameworks, and collaborative efforts to address ethical concerns and regulate Deepfake content. This systematic review explores the landscape of deep fake image generation, detection techniques and challenges, in addition to ethical considerations. By synthesizing existing research, we aim to provide insights into deep-fake technology's advancements, limitations, and societal implications. This review underscores the urgent need for interdisciplinary collaboration and robust frameworks to address the multifaceted issues surrounding deep fakes in the digital age.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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