Bookmarked The Tom Cruise deepfake that set off ‘terror’ in the heart of Washington DC (ABC News)

When Chris Ume unleashed a deepfake video of Hollywood star Tom Cruise, he had no idea the reaction it would provoke.

With the popularity associated with Deepfake Tom Cruise videos, Mark Corcoran and Matt Henry take a dive into the world of deepfakes. They breakdown the use of neural networks used to train a model to build a deepfake and the dramatic changes even from a year ago. Chris Ume provides a short sample:

In the Foreign Correspondance episode, Hamish McDonald demonstrates how these avatars are billed as supporting training videos, however the prospect of ‘synethic media’ for politics and pornography is always haunting.

If one person with a computer and an internet connection can make a convincing Tom Cruise, Ferraro fears the risks the technology could pose in the hands of well-resourced actors.

“Chris Ume said it took him a couple of months to train the algorithms and then he estimates about 24 hours to create a minute of video,” he says. “But imagine if you’re the intelligence services of China’s People’s Liberation Army, known as the 2PLA. They could put 10,000 man hours against the creation of a deepfake tomorrow.”

Although there are efforts to set standards and create detection tools, this is often a cat and mouse game. In addition to this, there are concerns about what this might mean notions of truth. As Sam Gregory suggests in regards to Phyo Min Thein’s video:

while the usage of deepfakes to create nonconsensual sexual images currently far outstrips political instances, deepfake and synthetic media technology is rapidly improving, proliferating, and commercializing, expanding the potential for harmful uses. The case in Myanmar demonstrates the growing gap between the capabilities to make deepfakes, the opportunities to claim a real video is a deepfake, and our ability to challenge that.

This problem has been described as the liar’s dividend. This has two clear consequences: informational uncertainty and rhetorical cover.

Liked Opinion | This Video May Not Be Real (

In the video Op-Ed above, Claire Wardle responds to growing alarm around “deepfakes” — seemingly realistic videos generated by artificial intelligence. First seen on Reddit with pornographic videos doctored to feature the faces of female celebrities, deepfakes were made popular in 2018 by a fake public service announcement featuring former President Barack Obama. Words and faces can now be almost seamlessly superimposed. The result: We can no longer trust our eyes.

Watched The Shining Starring Jim Carrey from

Taking advantage of inexpensive and easy-to-use software, deepfake artist Ctrl Shift Face has replaced Jack Nicholson’s face wit

This is the incredible and interesting and dangerous thing about the combination of our current technology, the internet, and mass media: “a lying government” is no longer necessary — we’re doing it to ourselves and anyone with sufficient motivation will be able to take advantage of people without the capacity to think and judge.

Replied to This Deepfake of Mark Zuckerberg Tests Facebook’s Fake Video Policies (Vice)

A fake video of Mark Zuckerberg giving a sinister speech about the power of Facebook has been posted to Instagram. The company previously said it would not remove this type of video.

It is interesting that Canny AI did not quite capture the voice. I guess this is where the technology is at? What ever happened to Adobe VoCo? It would seem that from discussion on an Adobe Forum that it was only a thought experiment:

VoCo was presented in an ideas forum – nothing there was guaranteed to be developed or released, neither were any timescales given if they were to be. All sorts of things could get in the way of any of them, and clearly a few have in this case. You may be disappointed, but you’re going to have to get over it, I’m afraid.

And if you’re doing anything commercially viable, then yes, you budget for voices. If you took somebody else’s voice and ‘repurposed’ it for your own ends, you’ve effectively stolen from them, haven’t you? Simply by depriving them of work they might have otherwise got. I think that the other thing that’s possibly happened over VoCo is that somebody has realised this, and is somewhat concerned about the possible backlash – and I don’t blame them.(SteveG)

It would seem that others will come though, such as LyreBird.

Jason Kottke provides a further discussion which was also interesting.

Listened Have we lost our sense of reality? from Radio National

Are the systems we’ve developed to enhance our lives now impairing our ability to distinguish between reality and falsity?


Dr Laura D’Olimpio – Senior Lecturer in Philosophy, University of Notre Dame Australia

Andrew Potter – Associate Professor, Institute for the Study of Canada, McGill University; author of The Authenticity Hoax

Hany Farid – Professor of Computer Science, Dartmouth College, USA

Mark Pesce – Honorary Associate, Digital Cultures Programme, University of Sydney

Robert Thompson – Professor of Media and Culture, Syracuse University

This is an interesting episode in regards to augmented reality and fake news. One of the useful points was Hany Farid’s description of machine learning and deep fakes:

When you think about faking an image or faking a video you typically think of something like Adobe Photoshop, you think about somebody takes an image or the frames of a video and manually pastes somebody’s face into an image or removes something from an image or adds something to a video, that’s how we tend to think about digital fakery. And what Deep Fakes is, where that word comes from, by the way, is there has been this revolution in machine learning called deep learning which has to do with the structure of what are called neural networks that are used to learn patterns in data.

And what Deep Fakes are is a very simple idea. You hand this machine learning algorithm two things; a video, let’s say it’s a video of somebody speaking, and then a couple of hundred, maybe a couple of thousand images of a person’s face that you would like to superimpose onto the video. And then the machine learning algorithm takes over. On every frame of the input video it finds automatically the face. It estimates the position of the face; is it looking to the left, to the right, up, down, is the mouth open, is the mouth closed, are the eyes open, are the eyes closed, are they winking, whatever the facial expression is.

It then goes into the sea of images of this new person that you have provided, either finds a face with a similar pose and facial expression or synthesises one automatically, and then replaces the face with that new face. It does that frame after frame after frame for the whole video. And in that way I can take a video of, for example, me talking and superimpose another person’s face over it.