Deepfake is a deep learning technology which enables realistic face-swapping on existing videos. In this blog, we will discuss deepfake technology, the way it works and its future potential.

A Generative Adversarial Network (GAN) is an unsupervised machine learning algorithm that can be used for image generation, image editing and similar tasks where one needs to generate images from a set of samples.

GAN has two networks – Generator and Discriminator. The generator network produces fake images while the discriminator tries to discriminate between real and fake images. As training progresses, the discriminator becomes better at its job of identifying fake images and eventually the generator also improves its output by generating more convincing images.

GANs were introduced by Ian Goodfellow et al in 2014 and are capable of producing realistic looking images of human faces, cats, cars etc. They have been used in various fields like image generation, text-to-image synthesis, super resolution etc.

Deepfake is a deep learning technology that enables realistic face-swapping on existing videos.

Deepfake technology is based on the concept of generative adversarial networks (GANs). A neural network is trained to recognize the difference between real and fake images. This neural network then generates new images similar to the ones it observed in training. The discriminator (the neural network) learns from the errors and then creates new images with improved realism to fool the discriminator. This process continues over a course of time till the discriminator cannot differentiate between real and fake images.

The technology has very high potential but is also highly debatable and can be misused if not regulated properly.

Deepfake is a deep learning technology that enables realistic face-swapping on existing videos. It has been around for over a year now, and has recently gained popularity because of the many face-swapped celebrity videos on the internet.

Deepfakes are created using deep learning techniques like Generative Adversarial Networks (GANs). A GAN is a two-part AI system that can generate an image similar to real images. It consists of two networks – Generator and Discriminator – competing with each other. The Generator network produces images while the Discriminator network evaluates them, and then provides feedback to the Generator network. The competition continues until the generator produces an image that passes off as real to the discriminator network.

Deepfakes are the latest technology that uses deep learning algorithms to create fake videos. It is a combination of two words, “Deep Learning” and “Fake”. In this article, we will discuss what deepfake technology is, how it works and its potential use.

Fake videos, pictures and audio are all around us. They are often used for propaganda purposes or for spreading misinformation about political leaders or celebrities. However, creating fake videos has always been a difficult task. While photoshop has made creating fake images easier, fake videos have always been difficult to create because the human face is very complex with a lot of moving parts and expressions.

Deepfake was first created by an anonymous user on Reddit in 2017 just for fun and basically to prank people in video chatrooms by swapping faces with celebrities. Since then it has evolved a lot thanks to the development of AI technologies like deep learning. Now anyone can create their own deepfakes using software like FakeApp which uses machine learning techniques to create deep fake videos.

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Deepfake Technology has been used to create fake news and spread misinformation. Deepfake can be used to create fake videos, conversations, movies and much more with the help of images and videos. The word deepfake is a combination of two words – deep learning and fake. It uses artificial intelligence (A.I) algorithms to generate photorealistic videos such as facial expressions, lip sync or body movement based on the training data sets.

Deepfake technology has been named as one of the top ten emerging technologies in 2019 by World Economic Forum (WEF). It can be used for both good and bad purposes. Deepfakes are mainly created using machine learning algorithms and developed by Google’s Tensorflow and Facebook’s PyTorch frameworks. Deepfakes can be created using multiple neural networks like generative adversarial network (GAN), autoencoders, variational autoencoders (VAE), etc., Depending on whether they are created using one or more neural networks, deepfakes can be classified into two types: single-network deepfakes and multi-network deepfakes.