Deepfakes Are Bad but What Are Some of the Possible Advantages?
What are Deepfakes? Can we ever really trust anything we see on TV or film ever again?
As it turns out those with the means, and motive, have been doctoring photos and video footage for decades. It's nothing new.
But Deepfakes, powered by AI and ML, have made it possible to produce almost perfect fake footage of public figures in, shall we say, compromising situations. But is this necessarily a bad thing?
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What are Deepfakes?
Deepfakes, construction of the terms deep learning and fake, is a technique of human image synthesis based on AI. According to Wikipedia:
"It is used to combine and superimpose existing images and videos onto source images or videos using a machine learning technique known as generative adversarial networks. The phrase "Deepfake" was coined in 2017.
This technique is incredibly effective and is becoming more advanced, and difficult to spot as time goes by. Deepfakes has been used in the past to create fake celebrity pornographic videos.
It has also been used to create fake news and other malicious, or purely satirical, hoax footage of other prominent individuals with another completely fabricated dialog. A prime, and frankly hilarious, example of this was the recent Deepfake video of Mark Zuckerberg.
Work on Deepfakes has taken place mainly in academic research and by amateurs in online communities. It is also thought that government agencies like the CIA or the UK's GCHQ have used the technique for propaganda purposes.
Who created Deepfakes?
Work on Deepfakes is actually nothing new. While nowhere near the sophistication of current technology, the field of "computer vision" has been around since the 1990s.
A subfield of computer science, it combines AI and computer processing of digital images and videos to create new artificial media. One notable early academic project was called the Video Rewrite program that was published in 1997.
It was able to modify existing video footage of a person speaking with new doctored dialog. It used machine learning to completely automate facial reanimation.
More modern academic, and amateur work in this field has focussed more on making the process simpler, faster and more accessible.
For example, the Face2Face program, published in 2016, modifies video footage of a person’s face to depict them mimicking the facial expressions of another person in real time.
Another example, the "Synthesizing Obama" program published in 2017, really showed the potential for this technology. But it can be argued the bulk of the most important work is from amateurs.
Before it was banned from Reddit, the r/deepfakes subreddit consisted of 'homemade' Deepfake content users had created. This community, while the content was primarily pornographic in nature, really showed how Deepfakes can be relatively easily created given enough knowledge and the right software.
How do Deepfakes work?
Deepfakes work by exploiting our natural tendency to believe what we are seeing if it is imperceptibly different from what we would consider real footage. It also attempts to 'hack' into someone's confirmation bias on a given subject - especially if the subject of the Deepfake is political in nature.
It is most effective if the viewer has never seen the original footage - for obvious reasons.
As for their actual creation, this tends to involve the use of two Generative Adversarial Networks (GANs). These two machine learning models fight out in a zero-sum game to create fake footage and have the other attempt to detect that it is forged.
The forger creates fakes until the other ML model can't detect the forgery. These systems work best when there is a wealth of footage and images of the video subject.
This is the main reason the most prominent Deepfakes tend to involve politicians or celebrities.
Advantages and disadvantages of Deepfakes
The disadvantages of something like Deepfakes are pretty obvious. From the possibility of creating fake incriminating evidence to "fake news" and propaganda, this technology can easily be used for nefarious purposes and blackmail.
But, it could have its advantages too. One interesting take on this comes from TowardsDataScience.com.
In essence, satirical Deepfake videos, whilst visually convincing to the eye, are obviously fabricated. This, as well as the rise of "Fake News", has opened up people's eyes to the fact it is possible and we should take everything 'with a pinch of salt'.
Warning: The following video contains some mild language - It is George Carlin.
"So thank Deepfakes, for making us aware of this, making us realize once again that we can’t take everything we see and hear for granted. For creating a problem for us to solve, early on, before it becomes so big, and has influenced so many of us incorrectly, that it’s too late.
It will take time. A new skill we must all learn. So doubt that next video you see on the Internet. Hell, doubt everything you see, read, or hear. Be more critical! Think for yourself."
We couldn't put it better ourselves. After all, if something seems too good, or bad for that matter, to be true, it probably needs to be questioned.
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