Deepfake is a term that originated from „deep learning“ and „fake“, referring to the use of artificial intelligence technologies, particularly deep learning algorithms, to manipulate or fabricate visual and audio content with high precision.

Deepfakes are typically created by employing a type of neural network known as an autoencoder to learn a representation of an individual’s face, voice, or other identifiable features. The system is trained on a dataset of images or sounds and then used to generate new content that mimics the learned attributes. This content can be overlaid onto existing video or audio, effectively creating a believable forgery.

Deepfakes have gained notoriety for their potential misuse, such as spreading disinformation or creating convincing, yet fraudulent, video or audio content. The realism of deepfakes raises serious concerns about trust and authenticity in digital media. However, the technology also has legitimate uses, such as in film production or for creating synthetic training data for machine learning models.

Due to the increasing prevalence and sophistication of deepfakes, efforts are being made to develop technology that can detect and counter these manipulations, with an aim to protect individuals and society from potential harm.

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