Deepfakes and AI: Understanding the Technology and Its Implications
Deepfakes, a compound word fusing “deep learning” and “fake”, are swiftly pervading the digital realm, eliciting both intrigue and alarm among specialists and the wider public. As the progression of artificial intelligence (AI) technology forges ahead, the capacity to fabricate strikingly authentic yet wholly synthetic videos, images, and sounds has become more within reach. This article explores the technical underpinnings of deepfakes, their potential applications, and the serious implications they bear, encompassing both the beneficial and the detrimental.
To grasp the essence of deepfakes, it is paramount to understand the foundational technology that allows their creation — a branch of AI known as deep learning. Deep learning utilizes algorithms named artificial neural networks, which draw inspiration from the structure and function of the human brain, to identify patterns in data. Based on these patterns, it can make predictions or decisions without the need for explicit programming[¹^].
When it comes to deepfakes, the deep learning algorithm that is typically employed is known as a Generative Adversarial Network (GAN). A GAN is composed of two elements: a generator, which crafts the fake images, and a discriminator, whose job it is to differentiate the faux images from the real ones[²^]. These two components work in conjunction, engaging in a form of competition with each other, to yield ever more persuasive fakes.
The process begins with feeding the GAN vast amounts of data, such as thousands of images or video frames of a specific person. The generator tries to create a fake image that matches the data, while the discriminator evaluates the image. If the discriminator can tell that the image is fake, it sends feedback to the generator. The generator then uses this feedback to improve the next image, and the process repeats. Over time, the GAN becomes…
