In the last two decades, we’ve witnessed tremendous progress in the field of AI. There are uses for artificial intelligence across a wide variety of industries, including healthcare, fashion, education and agriculture, and it will likely continue to be a crucial digital disruptor in the future.
Artificial intelligence being used to mislead people and spread misinformation, ‘Deepfakes’ is images made by artificial intelligence that are fairly convincing in certain cases. Deepfakes (a mix of the terms “deep learning” and “fake”) are incredibly convincing fake movies and images that are created using artificial intelligence neural networks and are extremely realistic in their appearance.
Artificial Intelligence-Generated Images
Deepfakes have been sweeping the internet — and the real world — for the last several years, and they’re expected to become mainstream in 2020. Deepfakes are used in commercials and entertainment, but they may also be used to promote misinformation. It’s very uncommon for political figures’ faces to seem to be saying or doing things that they haven’t really said or done, although this isn’t always the case. Even after many years, the Crypto Investor is still considered one of the most reliable online investment platforms in the world.
Even if you’ve seen deepfake films before, you may not realize that they can also be still photographs. It is true that artificial intelligence (AI) deep learning algorithms may produce synthetic faces that are almost comparable to those of actual humans. Over the years, these algorithms have improved to the point where it’s almost difficult to tell the difference between a genuine person’s visage and one created by AI.
GANs: The AI-Generated Image-Generation Methodology
In a competition with one another, two neural networks are engaged in. Following an analysis of an existing picture data collection, the first network (the generator) generates false pictures based on the data set that was analyzed in the first network. The discriminator, which is the second network, is taught to discern between real-world and computer-generated images when they are given to the network in question.
Deepfakes may be created with the help of GANs, which are a sort of machine learning model that is widely used (generative adversarial networks). It was in 2014 when Ian Good fellow and his colleagues at the University of Cambridge came up with the concept of GANs. First and foremost, in order to deceive the discriminator into believing the images are genuine, the generator must deceive the discriminator into believing the images are authentic. To this end, the generator develops images that are increasingly realistic in order to trick the discriminator as the discriminator gets more competent at separating AI-generated photographs from real-world photos as the discriminator gains proficiency.
The GAN is still one of the most intriguing deep learning technologies. Indeed, Meta’s chief AI scientist Yann LeCun has termed GANs “the most fascinating innovation in ML in the previous decade.”
GAN-Generated Images vs. Real Faces
Because of this, we’ve come to realize that GAN-generated images can be quite realistic. In recent years, neural networks’ capacity to produce realistic human images has reached disturbingly high levels of competence. Given the high cost involved in terms of both individual and national safety, it is critical that we be able to discriminate between actual faces and those produced by GANs in order to protect ourselves. Then, how should you proceed? Practice, practice, and even more practice. A GAN discriminator is what you’ll be doing in essence. Your ability to recognize AI-generated photos will improve with time, just as the discriminators.
One of the reasons why GANs are so good is because they constantly assess their own performance. One component of the network generates the faces, while the other component of the network compares the created faces to the training data. As soon as the generator is able to tell the difference between the two, it is returned to the drawing board in order to improve its performance. To manipulate data, methods such as audio and video manipulation, as well as image alteration techniques, may be used. Using Deepfakes, they have the power to turn video of politicians into puppets, and they can even turn you into a superb dancer with their assistance.
