Read Deep fake Quotes and what it is full information on whoisidentity.com.
Love and hatred are two quirky birds that are often seen fluttering in the same heart.
Only around 90% of what I say should be taken seriously. Don’t trust a word I say. I might be tampering with you and the rest of the world the whole time.
Kayne West
My greatest pain in life is that I will never be able to see myself perform live.
Kayne West
Deepfake Quote
If some extreme organisation develops a deepfake video of me forcibly attempting to have sex with a lady and posts it on the internet in an effort to ruin my character, you will have no way of knowing it’s not me. While there is nothing wrong with having sex (with the exception of paedophilia, betrayal, and promiscuity), consent is the boundary that separates human conduct from bestiality. All of my thoughts and ideas would suddenly lose their significance in your eyes. The only thing that may – just might – make you believe your eyes is your knowledge of my work. That is, however, the type of society we are going into, where anybody can make up any kind of film of someone to harm their reputation… We must continue with this in mind. We must raise our children with all of the courage we have to face the dark side of technology without committing suicide.
Abhijit Naskar, The Gospel of Technology
What is Deepfake?
Deepfakes (a combination of “deep learning” and “fake”) are synthetic media in which a person’s likeness is substituted in an existing photograph or video.
While making false information is not new, deepfakes use advanced machine learning and artificial intelligence methods to edit or produce visual and auditory content that is more readily deceiving.
Deep learning is used to construct deepfakes, which entails training generative neural network designs like autoencoders or generative adversarial networks (GANs).
Deepfakes have gotten a lot of press for their usage in child sexual abuse films, celebrity pornography, revenge porn, fake news, hoaxes, bullying, and financial fraud. This has prompted business and government efforts to identify and prohibit their usage.
How deepfake videos/Photos are made?
Deepfakes use an autoencoder, which is a form of neural network. An encoder reduces a picture to a lower-dimensional latent space, while a decoder reconstructs the image from the latent representation. Deepfakes use this architecture by encoding a person in the latent space using a universal encoder.
Key characteristics of their facial features and body posture are included in the latent representation. After then, a model trained particularly for the target may decode it. This implies that the target’s specific information will be placed on the latent space’s underlying face and body traits from the original video.
The addition of a generative adversarial network to the decoder is a common improvement to this design. In an adversarial interaction, a GAN trains a generator, in this instance the decoder, and a discriminator.
The generator generates new pictures from the source material’s latent representation, while the discriminator determines whether or not the image is created. Since a result, the generator produces pictures that closely resemble reality, as any flaws would be detected by the discriminator.
In a zero-sum game, both algorithms advance with time. Deepfakes are difficult to resist because they are continually changing; if a flaw is identified, it may be fixed.