Computer
Suha Mohammed Saleh; Abdulamir A. Karim
Abstract
From big data analytics to computer vision and human-level control, deep learning has been effectively applied to a wide range of complicated challenges. However, these same deep learning advancements have also been used to develop malicious software that threatens individuals' personal data, democratic ...
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From big data analytics to computer vision and human-level control, deep learning has been effectively applied to a wide range of complicated challenges. However, these same deep learning advancements have also been used to develop malicious software that threatens individuals' personal data, democratic processes, and even national security. Apps backed by deep learning have lately appeared, with deepfake being one of the most notable. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. One of the fields that deep learning accomplished major success is face synthesis and animation generation. On the other hand, it can create unethical software called deepfake that presents a severe privacy threat or even a huge security risk that can affect innocent people. This work introduces the most recent algorithms and methods used in deepfake. In addition, it provides a brief explanation of the principles that underpin these technologies and facilitates the development of this field by identifying the challenges and scopes that require further investigation in the future.
