I’m pretty good at photoshop. Small tweaks to photos and graphics is a useful skill to have.

I’m also pretty good at video editing. Small changes to a video is also a useful skill to have.

But what if I could combine those skills? What if changes made to a video were less CGI and more subtle to the point where you realistically would not know it’s edited?

That’s where Deep Fakes are starting to draw concern.

A few years ago, it became clear that Photoshop-ing of video, in a way that looks indistinguishable from authentic video, was quickly approaching. When it does, it will have a serious impact on visual evidence and specifically our ability to rely on them for facts and to make judgements. A world where video is duplicitous will delegitimize even genuine visual evidence and society will veer to the negative aspects of tribalism to help decide what is their version of “truth”.

Perhaps the true nature of the impact that Deepfakes have had on the current state of web video can be articulated in the belief shared by experts that the oncoming era of video streaming is going to feature highly realistic Deepfake content, with an endless combination of superimposed audio, videos and images.

Considering the stakes, knowing what Deepfakes are is of the utmost importance. Unlike the CGI that we’ve grown so accustomed to (thank you, Hollywood), Deepfakes make use of generative adversarial networks (abbreviated as GANs), in which two Machine Learning models work simultaneously and in concert to create convincing forgeries.

Usually, the larger the pool of training data is, the more convincing the Deepfake video is. This is why politicians and celebrities have been the most common targets of the first generation of Deepfakes; there’s already a massive amount of video footage available about them publicly.

Once you get ahead of the learning curve, the possibilities of the forgeries are endless. You can poke fun at Donald Trump, or you can make Nicholas Cage play roles he’s never played before. Realistically speaking; however, deepfakes are more likely to spawn hatred and disinformation, which can lead to dangerous consequences in a world already being overrun by “fake news”.

“Aha!”, you say. “This is a job for blocccckchainnnnn!”

And, the answer is . . .  maybe.

However, blockchain’s utility really shines when combined with other new age technologies.

Currently, the cybersecurity realm has been rampant with the inclusion of AI and blockchain, and both of these technologies can help neutralize the threat of Deepfakes.

As far as the blockchain technology is concerned, it can play a vital role in the authentication of digital identities, particularly when access to sensitive information is involved, such as financial details or social security numbers.

Furthermore, the authentication of suspicious videos and audio files can be done through a blockchain application, where the files can be compared against their original counterparts. In essence, blockchain technology could be the very tool that separated the wheat from the chaff, which is to say, the forgeries from the truth.

There are multi-stakeholder environments where aural and visual evidence are critical records for what occurred in the event of a situation. They can absolve and implicate and help neutral parties earnestly unpack what happened, weighing all the evidence, of which video is one important part.

Take, for example, police departments (where officers wear body cams) and the numerous stakeholders that exist when a shooting occurs. These could include the officer involved, investigators, the person who was shot and his/her legal team, prosecutors, judge/jury, watchdog groups, the media, and the general public. Many of these groups serve as an important part of our checks-and-balances system: how can each of these stakeholders have confidence in the veracity of the video?

A solution to the above law enforcement scenario is where videos are fingerprinted at the source recorder, the police body cameras. These fingerprints, or hashes, are written to an immutable ledger or blockchain from the recorder itself as most modern police body cameras have a wireless connection. As the video is downloaded from the device, uploaded to the cloud, clipped and shared, each event is written to a smart contract (a transparent agreement that is part of many blockchains). This creates an audit trail of the file and rehashing throughout the process to make sure the file’s integrity permeates.

When the file is shared and played by a stakeholder, the fingerprinting process is rerun or rehashed, and the fingerprints are compared to the ones in the smart contract. Either they match or they do not match: it is binary. The video is authentic or the video has been altered.

As the stakeholders should have more confidence in the immutability of a blockchain than the security policies and practices of a biased party, the doubt in the legitimacy of the video will recede. A key premise of blockchain design is trust-minimization. It seeks to create a secure environment between people and things (like Internet-enabled devices) and allows third parties to confidently transact with each other.

When it comes to the use of Artificial Intelligence in fighting against Deepfake technology, machine learning algorithms can play a vital part in recognizing patterns in large amounts of data, via specific classification techniques.

Have blockchain work with machine learning, which can play a particularly important role in detecting fake audio files from the authentic files, and perhaps we have a chance at retaining some veracity in evidence.

Although the world might seem bent on walking the path to a future where the invasion of privacy is a societal norm, staying cautious about terrifying technologies such as Deepfake, is the first step on the long path to attaining cybersecurity.