A friendly introduction to Deepfake AI | Best practices to detect Deepfake AI

Deepfake AI generated photo

While discussing generative AI, you may have heard of the terms deepfake or deepfake artificial intelligence. And in this video, we'll discuss exactly what deepfakes are, how deepfakes are created, and how you can detect deepfake content. Let's start off with discussing what exactly deepfake AI means. This is a type of artificial intelligence used to create convincing images, audio, and video hoaxes. With deepfake AI, you produce content that looks very real but is not actually real and is typically used to mislead and misinform the public.

How Deepfakes are being used nowadays:

Let's say you have a celebrity figure endorsing a product or voicing an opinion when he or she did not actually do so. That's an example of deepfake AI. Deepfake refers not only to the artificial intelligence system, but also to the bogus content that is generated by such systems. The highly realistic and often convincing digital manipulations that these systems can create. Now, the term deepfake is a portmanteau of deep learning, that is, neural network models and fake.

So, it’s these neural network models that are used to generate these convincing fakes. Deepfakes can be categorized into several different types based on the nature and purpose of the manipulation. For example, you can have face swaps, which is one of the most common types of deepfakes, where the face of one person is replaced with another's in a video. It's often used to create fake celebrity videos, or to put a person's face on another's body in a compromising or unethical context. In lip syncing deepfakes, a person's lips are manipulated to match altered, or entirely new audio.

This type can be used to make it appear as though someone is saying something they never actually said. As you might imagine, this can be particularly dangerous for spreading misinformation. Here, the deepfake technically maps a voice said on the recording to the person's in the video, making it appear as though the person itself in the video is talking about the words of the recording. In case, if the audio itself is made from deepfake, then the video made from deepfake adds an extra layer of deception to the overall watching experience. This brings us to the next kind of deepfake, voice cloning, where AI is used to clone a person's voice, allowing the creator to generate audio recordings of the person saying things that they never actually said.

How does deepfake AI work:

These are audio deepfakes. The AI creates a model based on the vocal patterns and uses that model to make the voice say anything the creator wants. Another category of deepfake is expression manipulation, where a person's facial expressions or body language is altered in an existing video. It can be used in the film industry for special effects, but also has the potential for misuse in creating misleading content. Deepfakes can also generate entirely synthetic representations of people who do not exist in real life, often used in creating virtual influencers or fake personas for various purposes, both benign and malicious.

Deepfakes can also be used for scene generation or alteration, which involves creating or altering entire scenes or backgrounds. This can range from changing the weather in a video scene or creating entirely fictional locations. The neural network model that's widely used to generate deepfake content is the GAN or the generative adversarial network. Here, the generator uses the training data to create the fake digital content. The discriminator acts as a classification model and analyzes how fake or real the generated content is. The discriminator penalizes the generator for generating content that's very obviously fake, and this causes the generator to improve the content that it generates, making it more and more realistic.

Let’s say you’re using a GAN to generate a deepfake photo. The GAN may use photos of the target from different angles to capture the essential details before generating its deepfake. Let’s say you’re using a GAN to generate deepfake video. The GAN may analyze entire behavior, movement, and speech patterns of the target before generating its video. Deepfakes have both benign and malignant use cases. Deepfakes can be used for generating art, and that is a positive use case of deepfakes.

But deepfakes are widely used in causing reputational harm. You have a celebrity or a well-known persona saying or doing something that he or she did not actually do. This can mislead the public and cause tremendous reputational harm. Deepfakes can also be used for entertainment, Hollywood movies, and video games clone and manipulate actors’ voices for certain scenes. Deepfakes can also be used for satire and parody content, in which the audience understands that the video isn't real but enjoys the humorous situation anyway. Deepfakes are also used for false evidence and fraud.

False evidence involves the fabrication of false images or audio that can be used as evidence, implying guilt or innocence in a legal case. Deepfakes can be used to impersonate individuals to obtain personally identifiable information, such as bank account and credit card numbers, in order to perpetuate fraud. Deepfake videos of politicians or trusted sources can be used to sway public opinion and cause reputational harm and manipulate the public into believing that a person did something that he or she did not actually do.

Deepfake materials can be used to affect a company's stock price. For example, you might have a video of a chief executive officer saying or doing something that causes stock prices to go up or down in order to protect yourself against misleading or malicious information, it's important for you to develop the skill to detect deepfake video and audio. 

How to detect Deepfake AI:

Let’s discuss some attributes that you can look for to detect a deepfake video. If you find that the individual in the video is moving in an unusual way or has awkward facial positioning, that video is likely a deepfake.

Facial movements and body movements are not always realistic in deepfake videos. This is a telltale sign that you should watch out for unnatural facial or body movement. Deepfake videos are very realistic, but sometimes you might find unnatural coloring or inconsistent audio. The audio isn’t always very smooth, another telltale sign. Or you might find that the video looks odd when you zoom into the video or zoom out. Maybe the people in the video don't blink. All of these are signs that you can use to detect a deepfake video.

How to detect Deepfake text?

Now, how do you detect deepfake text? Check the generated text to see whether there are misspellings or sentences that are unnatural, not something that a human would speak or write. If you’re receiving a deepfake email, check the source email address to see whether it looks suspicious or it's very close to something that you would trust.

You might receive an email from somebody that you actually know, but the phrasing does not match that person's personality or temperament. Another telltale sign that this might be a deepfake. And finally, the content of deepfake text may not always make sense in the context of the sender, so beware of out-of-context messages. They’re likely to be deepfakes.

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