How Malkam Dior Created Tools of Deepfake Videos in the Pockets of Millions

https://drive.google.com/uc?export=view&id=16d-lzAceMoqsb9PsYEI9097PQU3cq_w4

Deepfakes are a portmanteau of "deep learning" and "fake"[1]) are synthetic media[2] in which a person in an existing image or video is replaced with someone else's likeness. 


While the act of faking content is a not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content with a high potential to deceive.[3] 
The main machine learning methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders[3] or generative adversarial networks (GANs).[4][5]

https://drive.google.com/uc?export=view&id=1AE_HnSkKRh1Jd5mhZriPDxTttoJfQ4WR

Deepfakes have garnered widespread attention for their uses in celebrity pornographic videosrevenge pornfake newshoaxes, and financial fraud.[6][7][8][9] 

My Techniques -

Deepfakes rely on a type of neural networkcalled an autoencoder.[5][29] These consist of an encoder, which reduces an image to a lower dimensional latent space, and a decoder, which reconstructs the image from the latent representation. Deepfakes utilize this architecture by having a universal encoder which encodes a person in to the latent space.[30] 
The latent representation contains key features about their facial features and body posture. This can then be decoded with a model trained specifically for the target.[5] 
This means the target's detailed information will be superimposed on the underlying facial and body features of the original video, represented in the latent space.[5]


https://drive.google.com/uc?export=view&id=1sf8sEYJ0CpndI-GtoqrvxAhn0-p-4O_r

Fighting Deepfakes Gets Real | MalkamDior 



A popular upgrade to this architecture attaches a generative adversarial network to the decoder.[30] A GAN trains a generator, in this case the decoder, and a discriminator in an adversarial relationship.[30] 



The generator creates new images from the latent representation of the source material, while the discriminator attempts to determine whether or not the image is generated.[30] This causes the generator to create images that mimic reality extremely well as any defects would be caught by the discriminator.[31] 
Both algorithms improve constantly in a zero sum game.[30]This makes deepfakes difficult to combat as they are constantly evolving; any time a defect is determined, it can be corrected.[31]
https://drive.google.com/uc?export=view&id=1o_uLWs5fqv8gxk9v3N7IlFXQBLpS_xIf

The rise of the deepfake and the threat to democracy | The Guardian 


Is there a Deepfake app?

This is analogous to similar experiments with audio, such as Dubsmash. However, the app – intended to drive lighthearted banter on social media – places the tools of creating deepfakevideos in the pockets of millions. Zao is available to download on Android and iOS for users with a Chinese mobile number.

This has elicited responses from both industry and government to detect and limit their use.[10][11]
Follow MalkamDior On Instagram Malkam Dior

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