The very concept of teaching with deep fake technology may be unsettling to some. After all, deep fake technology, which utilizes AI and machine learning and can alter videos and animate photographs in a manner that appears realistic, has frequently been covered in a negative light. The technology can be used to violate privacy and create fake videos of real people. However, while these potential abuses of the technology are real and concerning that doesn't mean we should turn a blind eye to the technology's potential when using it responsibly, says Jaime Donally, a well-known immersive learning expert. "Typically, when we're hearing about it, it's in terms of the negative – impersonation and giving false claims," Donally says.

The following is the June 3, 2022 Congressional Research Service In Focus report, Deep Fakes and National Security. "Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. How Are Deep Fakes Created? Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs).

When a video purporting to show New Zealand Prime Minister Jacinda Ardern smoking drugs surfaced on social media in recent months, experts quickly dismissed it as a fake. The video, which was viewed and shared thousands of times, showed a woman smoking from what appeared to be a crack pipe. The PM's face had been superimposed using artificial intelligence. But the video, created for YouTube, was convincing enough to the many who shared it. It was the latest example of how disturbingly authentic-looking videos can blur the lines between reality and fantasy.

The history of deep fake technology is surprisingly long. Researchers at academic institutions have been developing deep fake tech since the early 1990s. The idea is even older, as popular science fiction--like the 1987 film The Running Man--can attest. But deep fakes are no longer relegated to the realm of sci-fi; they are, in fact, more present in our daily lives than you might realize. It's easy to think of deep fakes as some sort of advanced CGI used to create highly realistic animated films or to replace established actors in a film or television series, especially in cases where actors pass away unexpectedly before filming is complete.

The past decade has witnessed the unprecedented march of technology and the opportunities, dangers, and disruptions that accompany it. In the last 4-5 years, a synthetic media technology (that uses machine learning techniques and is created by generative adversarial networks – GANs) known as deep fakes, has revolutionised the ways that digital media can be altered. The ability of state and non-state actors to generate, forge, and manipulate media has created clickbait headlines and fake news, 'terrorised women' by substituting faces to create fake porn, and abetted the spread of misinformation and disinformation. An opinion piece in the Washington Post has called this worrying trend of mass-scale manipulation the "democratisation of forgery". In the last 4-5 years, a synthetic media technology known as deep fakes, has revolutionised the ways that digital media can be altered.

Digital transformation is exponentially increasing the possible attack surface, creating new possibilities for the cyber-criminal sector. In addition to their ever-expanding arsenal of malicious software and zero-day risks, new technologies such as automated hacking, deepfakes, and weaponized AI are being contributed to their arsenal. Let's dive into the article to learn how these tools are a threat to the world today. What are the practical applications of automated hacking, and how can they influence your business? Hackers use programs like Shodan to compile a comprehensive list of internet-connected devices, including web servers, surveillance cameras, webcams, and printers.

"Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs). In the GAN process, two ML systems called neural networks are trained in competition with each other. The first network, or the generator, is tasked with creating counterfeit data--such as photos, audio recordings, or video footage--that replicate the properties of the original data set.

Your family history is probably in photographs of the past, but thanks to machine learning and an app, they could be somewhat living once again. Technology can do some remarkable things, but one of the more unusual ones in recent years may be the field of deep fakes. It's a technology concept you'd have probably heard of whereby machine learning connects with visual imagery to join the dots and get one image to resemble a next, often in video. It's what happens when the face of one person is matched to the scene or video of another, and has been found used across the world in various ways, from news to ads to film and art. And now, it's being used with family photos, as a family history app and service connects the world of deep fake technology to family photos, giving them some life and reanimating their imagery.