Pdf Exploring Deepfakes And Effective Prevention Strategies A

Bonisiwe Shabane

-Dec 4, 2025, 9:18 PM

pdf exploring deepfakes and effective prevention strategies a

Deepfake technology, powered by artificial intelligence and deep learning, has rapidly advanced, enabling the creation of highly realistic synthetic media. While it presents opportunities in entertainment and creative applications, deepfakes pose significant risks, including misinformation, identity fraud, and threats to privacy and national security. This study explores the evolution of deepfake technology, its implications, and current detection techniques. Existing methods for deepfake detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), are examined, highlighting their effectiveness and limitations. The study also reviews state-of-the-art approaches in image forensics, phoneme-viseme mismatch detection, and adversarial training to counter deepfake threats. Moreover, the ethical and legal challenges surrounding deepfakes are discussed, emphasizing the need for policy regulations and collaborative efforts between governments, tech companies, and researchers.

As deepfake technology continues to evolve, so must detection strategies, integrating multimodal analysis and real-time verification systems. This research underscores the importance of developing robust detection frameworks and public awareness initiatives to mitigate the risks associated with deepfakes. Future directions include enhancing detection algorithms through explainable AI, improving dataset quality, and integrating blockchain for digital content authentication. By providing a comprehensive analysis of deepfake creation, detection, and countermeasures, this study contributes to the ongoing discourse on synthetic media and its societal impact. Addressing these challenges requires interdisciplinary collaboration and continuous innovation to safeguard digital integrity and trust in the information ecosystem. Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET) Deep learning, integrated with Artificial Intelligence algorithms, has brought about numerous beneficial practical technologies. However, it also brings up a problem that the world is facing today. Despite its innumerable suitable applications, it poses a danger to public personal privacy, democracy, and corporate credibility. One such use that has emerged is deepfake, which has caused chaos on the internet.

Deepfake manipulates an individual's image and video, creating problems in differentiating the original from the fake. This requires a solution in today's period to counter and automatically detect such media. This study aims to explore the techniques for deepfake creation and detection, using various methods for algorithm analysis and image analysis to find the root of deepfake creation. This study examines image, audio, and ML algorithms to extract a possible sign to analyze deepfake. The research compares the performance of these methods in detecting deepfakes generated using different techniques and datasets. As deepfake is a rapidly evolving technology, we need avant-garde techniques to counter and detect its presence accurately.

Pdf Exploring Deepfakes And Effective Prevention Strategies A

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