Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes

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(as of Dec 24,2024 22:44:33 UTC – Details)

ASIN ‏ : ‎ B099MW4GQB
Publisher ‏ : ‎ Apress (July 15, 2021)
Publication date ‏ : ‎ July 15, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 22476 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 433 pages

In today’s digital age, the lines between reality and fiction are becoming increasingly blurred. With the advent of advanced technologies such as autoencoders and adversarial networks, it is now possible to create highly realistic fake videos and images known as deepfakes.

Autoencoders are a type of artificial neural network that can learn to encode and decode data efficiently. By training an autoencoder on a set of images or videos, it can generate new, realistic-looking content that is indistinguishable from the original data.

Adversarial networks, on the other hand, consist of two competing neural networks – a generator and a discriminator. The generator creates fake content, while the discriminator tries to distinguish between real and fake data. Through this iterative process, both networks improve their performance, resulting in highly convincing deepfakes.

While the technology behind deepfakes is impressive, it also raises serious ethical concerns. Deepfakes have the potential to spread misinformation, manipulate public opinion, and even be used for malicious purposes such as creating fake celebrity pornographic videos.

As we navigate this new era of digital deception, it is crucial to develop robust detection methods and educate the public about the dangers of deepfakes. By staying informed and vigilant, we can help prevent the spread of fake news and protect the integrity of our increasingly digitized world.
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