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

Comments

Popular Posts

911 Memorial Brooklyn Bridge Experience On Airbnb

911 Memorial Brooklyn Bridge Experience On Airbnb
Book An Experience With Google Scholar Malkam Dior

Follow my twitter or insta @malkamdior and come grow it with me, just say Hi ! 😊 See you 💜💜💜

Labels

Show more