What are deepfakes in AI?

Imagine strolling down a street, only to be startled by your face staring back at you from a tall billboard promoting a brand you don’t know about. Imagine the fear and helplessness of realizing that your image, your identity, can be manipulated and used against you without your knowledge or consent. This is the chilling reality of deepfakes.

Key takeaways:

• Deepfakes are synthetic media created using AI.

• These use GANs to generate content.

• Detecting deepfakes requires specialized methods.

• Creating deepfakes requires specialized knowledge, equipment, and software.

• Deepfakes can be used for both harmless entertainment and malicious purposes.

• Deepfakes pose significant risks, including misinformation and harassment.

Let’s explore the world of deepfake technology.

Deepfake technology explained

Deepfake is a term derived from deep learning and fake. It falls under the branch of artificial intelligence called deep learning and leverages deep neural networks called generative adversarial networks (GANs) to create fake images, videos, and sounds.

Deepfake technology, a sophisticated AI application, can manipulate or create highly realistic content, such as videos, audio, or images. This advanced technology can generate exact replicas of real individuals or events, making it difficult to distinguish between genuine and fake content. For instance, the fake video of President Obama https://www.youtube.com/watch?v=AmUC4m6w1wo , circulated by researchers from the University of Washington, showcased the potential for deepfakes to deceive viewers. The ability of deepfakes to manipulate media content raises serious concerns about the spread of misinformation, fraud, and identity theft.

“Like other forms of stealing, identity theft leaves the victim poor and feeling terribly violated.”

— George W. Bush

How do the deepfakes work?

Deepfakes are known to utilize two algorithms called a generator and a discriminator. In simple words, both compete against one another; the generator tries to fool the discriminator into believing it has created original content, while the discriminator tries to pinpoint whether the content created by the generator is fake or real. At first, the discriminator is not easily tricked, but with a few attempts by the generator, it begins to assume that the fake data is real. This process happens continuously, where the generator creates a training dataset based on a set output by identifying patterns in images/videos/sounds, while the discriminator differentiates fake content from genuine one until the discriminator can no longer set them apart. This iterative process elevates the generator’s ability to create fake content that the discriminator can’t spot. On the other hand, it also enhances the generator’s ability to identify fake data. The combination of a generator and discriminator is what constitutes a GAN in deep learning.

Curious about GANs? Check out the following captivating courses:

• Hands-On Generative Adversarial Networks with PyTorch

• Hands-On Generative Adversarial Networks with Keras

How to detect deepfakes

No matter how accurate an exact copy a deepfake may create of the original content, there are still ways to detect fake from the real content. These indicators include unusual or awkward facial positioning, weird facial or body movement, coloring inconsistencies, inconsistent audio, and people who don’t blink.

Deepfake prevention is possible using protective software from companies like Adobe, Microsoft, and Sensitivity.

How to create deepfakes

We can create deepfakes using the following three approaches:

• Deepfakes created from source videos: A neural network-based autoencoder exposes the source video’s facial expressions and body language and imprints them onto the fake video. The autoencoder does this by encoding the required attributes and decoding via a decoder to impose them on the fake video. These features can impersonate body language and create a fake scenario in the target video.

• Deepfakes created from the audio: A GAN copies a person’s audio by creating a model from it, which can be used to create new audio with that person’s voice.

• Lip sync: An extra layer of deception can be added by adding lip sync to a fake video generated by a GAN. Lip sync attaches a person’s voice to a video, and the audio and video used to create the lip sync can be faked. 

Technologies to make deepfakes

Common uses of the deepfake

Some of the applications of the deepfake are as follows:

• Deepfakes are used to create masterpieces of art.

• Customer support uses fake voices to prompt the listener to dial a certain extension or file a complaint at the end of a call.

• It’s widely used in the fashion industry to dress customers as models to see how they look in the latest attire.

• Deepfakes are also used to increase the resolution of low-quality images.

Deepfake dangers

Deepfakes also pose significant dangers, including:

• It can produce convincing fake news, spreading misinformation and confusion.

• Deepfake audio or video recordings can be used against specific people to undermine their reputations.

In conclusion, Deepfake technology, powered by GANs and deep learning, offers innovative opportunities and moral problems. It provides innovation in several areas, including art and customer service, but raises concerns about manipulation and false information. Despite detection advancements, addressing ethical concerns and promoting digital literacy is crucial for responsible deepfake use.