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Der erste und derzeit häufigste Einsatz von Deepfakes findet im Bereich des „​face swapping“ statt. Hierbei wird in visuellem Material (z. B. Videos oder Fotos). lundahoppet.se 1/5. Deepfakes – Perfekt gefälschte Bilder und Videos. Zusammenfassung. Gefälschte Bilder sind nichts Neues. Deepfake setzt sich zusammen aus den englischen Begriffen Deep Learning und Fake - also tiefgehendes Lernen und gezielte Fälschung. Es. Was sagt Obama da bitte über Trump? Und warum zum Teufel trägt Schaffi ein violettes Kleid? Echt oder Fake? Deepfake! Videos und Audios. Stattdessen ließ Buzzfeed Obama im vergangenen Jahr in einem Deepfake-​Video vor Deepfakes warnen: Existiert ausreichend aufgenommenes und.

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For instance, taking a video of people beating someone up in the street, and then creating a false narrative around that video — perhaps claiming that the attackers are immigrants to the U.

Detecting deepfakes is a hard problem. Amateurish deepfakes can, of course, be detected by the naked eye. Other signs that machines can spot include a lack of eye blinking or shadows that look wrong.

GANs that generate deepfakes are getting better all the time, and soon we will have to rely on digital forensics to detect deepfakes — if we can, in fact, detect them at all.

This is such a hard problem that DARPA is throwing money at researchers to find better ways to authenticate video.

However, because GANs can themselves be trained to learn how to evade such forensics, it's not clear that this is a battle we can win.

It's unclear. If we are unable to detect fake videos, we may soon be forced to distrust everything we see and hear, critics warn.

The internet now mediates every aspect of our lives, and an inability to trust anything we see could lead to an "end of truth. If we can't agree on what is real and what is not, how can we possibly debate policy issues?

Hwang thinks this is exaggeration, however. At the end of the day, the hype around deepfakes may be the greatest protection we have.

We are on alert that video can be forged in this way, and that takes the sting out of deepfakes. Politicians have been lying for as long as politics has existed, and the threat of deep fakes to democracy is overblown.

The more realistic threat, however, is the creation of deep fake pornography that puts a celebrity's — or maybe an ex-girlfriend's — head on the body of a porn star.

It's like revenge porn, only without the porn. All the deep-fake porn creator needs is a bunch of photos of the victim, easily taken from that person's social media feed, and a video of the victim's face that's several minutes long.

Even celebrities, many of whom are used to a certain amount of public hating, have expressed horror on discovering their heads superimposed on the body of a porn star in a raunchy video.

Sometimes deep fakes aren't about gaslighting a population, but about bullying or harassment. This seems like a far more probably outcome than those who carry on about deep fakes being an existential threat to democracy.

Are deep fakes legal? It's a thorny question, and unresolved. There's the First Amendment to consider, but then intellectual property law, privacy law, plus the new revenge porn statutes many states across the United States have enacted of late.

In many cases platforms such as Gfycat and Pornhub have actively removed deep fake porn videos from their websites, arguing that such content violates their terms of service.

Deep fakes of the pornographic variety continue to be shared on less-mainstream platforms. However, when it comes to political speech that is not of an abusive sexual nature, the lines get blurry.

The First Amendment protects the right of a politician to lie to people. It protects the right to publish wrong information, by accident or on purpose.

The marketplace of ideas is meant to sort the truth from falsehood, not a government censor, or a de facto censor enforcing arbitrary terms of service on a social media platform.

Think fake news videos--of the political deep fake variety--are a new thing under the sun? Think again.

For a generation after the invention of cinema, faking news videos in order to dramatize the real news was par for the course. At a time when film could take weeks to cross an ocean, filmmakers would dramatize earthquakes or fires with tiny sets to make the news more lifelike.

In the s, sending black-and-white photographs over transoceanic cables was the latest rage, and filmmakers would use genuine photographs to create their scenes of destruction.

That changed in the s, and it was the expectation of audience members that what they were watching was the genuine article. Got news?

Here are the latest Insider stories. More Insider Sign Out. Sign In Register. Sign Out Sign In Register. It's not just the weird, little-bit-off, not-quite-right videos produced by these increasingly sophisticated software programs.

Although, yeah, they can be unsettling. And it's not just the ethical dilemma in altering original photos and videos, either. Though that's definitely poking a hornet's nest.

Mostly, it's the whole idea that we are rapidly closing in on a point where we simply may not be able to trust our own eyes.

Is that photo a true depiction of its subject? Is that video? Does that face go with that body? Do those words go with that face? Way back in late , a Reddit user known as Deepfakes, according to Know Your Meme , unveiled some face-swapping pornographic videos — it's exactly as sad and lame as it sounds; someone's face, often a public figure, superimposed onto someone else's head — and the deepfakes frenzy began.

Shortly after, Deepfakes launched an app, FakeApp, and people jumped all over it. All sorts of memes from that and other programs — some funny, some just plain creepy, some worse — have been produced since.

The computer science used to create the programs behind these videos can be extremely complex, much more intense than what is used for simple deepfakes.

Intricate algorithms and computer science terms like generative adversarial networks GAN and deep neural networks pepper the academic papers of the more advanced video-editing techniques.

Generally, what these programs do is examine the video of a subject frame by frame and "learn" the subject's size and shape and movements so that they can be transferred to another subject on video.

Whereas deepfakes have been limited mainly to swapping out the subjects' faces, the more advanced programs can transfer full 3D head positions, including things like a head tilt or a raised eyebrow or a set of pursed lips.

Some work has been done on entire body movements. The more these programs detect, the more variables that these networks are fed and "learn," the more efficient, effective and realistic the videos become.

It's important to note that not all video and photo editing techniques based in artificial intelligence and machine learning are deepfakes. Those in academics who work in the field see deepfakes as amateurish, relegated to mere face-swapping.

A group at the University of California Berkeley is working on a technique that takes an entire body in motion — a professional dancer — and swaps it onto an amateur's body on video.

With a little AI wizardry, then, even someone with two left feet can at least appear to move like Baryshnikov. The Berkeley group detail its work in the paper, Everybody Dance Now.

The technique is not perfect, of course. But this is tricky stuff. Even pulling off a computer-generated moving face is difficult.

As of now, most AI-generated faces, even on deepfakes — especially on deepfakes — are obvious forgeries. Something, almost invariably, seems a little off.

I think the machine-learning system these days is still not able to capture all those details. Another new AI video-manipulation system — or, as its architects call it, a "photo-realistic re-animation of portrait videos" — actually uses one "source" actor that can alter the face on a "target" actor.

You, the "source" for example , move your mouth a certain way, computers map the movement, feed it into the learning program and the program translates it to a video in which Obama mouths your words.

You laugh, or raise your eyebrow, and Obama does, too. A paper on that process, known as Deep Video Portraits , was presented at a computer graphics and interactive techniques conference in Vancouver in mid-August , and reveals a place for the program: Hollywood.

Virtually every high-end movie production contains a significant percentage of computer-generated imagery, or CGI, from Lord of the Rings to Benjamin Button," the authors write.

The production of even a short synthetic video clip costs millions in budget and multiple months of work, even for professionally trained artists, since they have to manually create and animate vast amounts of 3D content.

Thanks to AI, we can now produce the same imagery in a lot less time. And cheaper. And — if not now, soon — just as convincingly. The process of manipulating existing video, or creating a new video with false images, as comedian Peele and others warn, can be downright dangerous in the wrong hands.

Some prominent actresses and entertainers had their faces stolen and weaved into porn videos in the most disturbing early examples of deepfakes.

Medienbildung: Deepfakes sind von Künstlicher Intelligenz manipulierte Videos. Dieser WissenPlusVideo-Beitrag erklärt die Technologie des. Mithilfe Künstlicher Intelligenz in es möglich, sogenannte Deepfakes, zu erstellen - realistisch aussehende Fake-Videos, in denen das Gesicht. Deepfakes videos

Deepfakes Videos - Navigationsmenü

Seit wird universitär und industriell daran geforscht generierte Gesichter zum Schutz der Identität einzusetzen. Digitaler Populismus vom Feinsten. Man stelle sich vor, was man Trump, Putin, Merkel, Un oder sonst wem mit solcher Technik wortwörtlich auf die Lippen legen könnte In: Vice. Leserempfehlung 6. Die ursprünglich unter anderem befürchtete Welle an politischen Deepfakes ist bisher ausgeblieben. Dieses Ergebnis wird mit Software erzielt, bei der Ansätze aus dem maschinellen Lernen verwendet werden.

Retrieved 16 October The Washington Post. NBC News. The Verge. ACM Trans. IEEE: — Business Insider Australia. Retrieved 27 August Retrieved 9 July Retrieved 4 May Retrieved 4 April Retrieved 12 December The Daily Dot.

Retrieved 22 December Online Tech Tips. Includes prebuilt ready to work standalone Windows 7,8,10 binary look readme.

Fast Company. Bibcode : arXivJ. Retrieved 3 April Retrieved 6 March Alan Zucconi. Rolling Stone. Retrieved 7 July Business Insider.

Retrieved 2 July Vice Media. Retrieved 10 September The employee has been fired". Retrieved 11 January Public Seminar. Retrieved 19 February Extinction Rebellion Belgium.

Retrieved 21 April Retrieved 20 October MIT Technology Review. Music Times. Retrieved 26 August Retrieved 18 November Retrieved 7 January Retrieved 13 September Berkeley News.

Retrieved 19 December Spiegel Online. Retrieved 28 November This makes a lot of people nervous, so much so that Marco Rubio, the Republican senator from Florida and presidential candidate, called them the modern equivalent of nuclear weapons.

Today, you just need access to our internet system, to our banking system, to our electrical grid and infrastructure, and increasingly, all you need is the ability to produce a very realistic fake video that could undermine our elections, that could throw our country into tremendous crisis internally and weaken us deeply.

Political hyperbole skewed by frustrated ambition, or are deepfakes really a bigger threat than nuclear weapons? To hear Rubio tell it, we're headed for Armageddon.

Not everyone agrees, however. I think they're concerning and they raise a lot of questions, but I'm skeptical they change the game in a way that a lot of people are suggesting.

Seeing is believing, the old saw has it, but the truth is that believing is seeing: Human beings seek out information that supports what they want to believe and ignore the rest.

Hacking that human tendency gives malicious actors a lot of power. We see this already with disinformation so-called "fake news" that creates deliberate falsehoods that then spread under the guise of truth.

By the time fact checkers start howling in protest, it's too late, and PizzaGate is a thing. Deepfakes exploit this human tendency using generative adversarial networks GANs , in which two machine learning ML models duke it out.

One ML model trains on a data set and then creates video forgeries, while the other attempts to detect the forgeries.

The forger creates fakes until the other ML model can't detect the forgery. The larger the set of training data, the easier it is for the forger to create a believable deepfake.

This is why videos of former presidents and Hollywood celebrities have been frequently used in this early, first generation of deepfakes — there's a ton of publicly available video footage to train the forger.

It turns out that low-tech-doctored videos can be just as effective a form of disinformation as deepfakes , as the controversy surrounding the doctored video of President Trump's confrontation with CNN reporter Jim Acosta at a November press conference makes clear.

Video clearly shows a female White House intern attempting to take the microphone from Acosta, but subsequent editing made it look like the CNN reporter attacked the intern.

The incident underscores the fears that video can be easily manipulated to discredit a target of the attacker's choice—a reporter, a politician, a business, a brand.

Unlike so-called "deepfakes," however, where machine learning puts words in people's mouths, low-tech doctored video hews close enough to reality that it blurs the line between the true and false.

FUD fear, uncertainty and doubt is familiar to folks working in the security trenches, and deploying that FUD as a weapon at scale can severely damage a business as well as an individual.

Defending against FUD attacks is very difficult. Once the doubt has been sowed that Acosta manhandled a female White House intern, a non-trivial portion of viewers will never forget that detail and suspect it might be true.

GANs, of course, have many other uses than making fake sex videos and putting words in politicians' mouths. GANs are a big leap forward in what's known as "unsupervised learning" — when ML models teach themselves.

This holds great promise in improving self-driving vehicles' ability to recognize pedestrians and bicyclists, and to make voice-activated digital assistants like Alexa and Siri more conversational.

Ordinary users can download FakeApp and get started creating their own deepfakes right away. Using the app isn't super-easy, but a moderately geeky user should have no trouble, as Kevin Roose demonstrated for the New York Times earlier this year.

Some work has been done on entire body movements. The more these programs detect, the more variables that these networks are fed and "learn," the more efficient, effective and realistic the videos become.

It's important to note that not all video and photo editing techniques based in artificial intelligence and machine learning are deepfakes.

Those in academics who work in the field see deepfakes as amateurish, relegated to mere face-swapping.

A group at the University of California Berkeley is working on a technique that takes an entire body in motion — a professional dancer — and swaps it onto an amateur's body on video.

With a little AI wizardry, then, even someone with two left feet can at least appear to move like Baryshnikov. The Berkeley group detail its work in the paper, Everybody Dance Now.

The technique is not perfect, of course. But this is tricky stuff. Even pulling off a computer-generated moving face is difficult.

As of now, most AI-generated faces, even on deepfakes — especially on deepfakes — are obvious forgeries.

Something, almost invariably, seems a little off. I think the machine-learning system these days is still not able to capture all those details.

Another new AI video-manipulation system — or, as its architects call it, a "photo-realistic re-animation of portrait videos" — actually uses one "source" actor that can alter the face on a "target" actor.

You, the "source" for example , move your mouth a certain way, computers map the movement, feed it into the learning program and the program translates it to a video in which Obama mouths your words.

You laugh, or raise your eyebrow, and Obama does, too. A paper on that process, known as Deep Video Portraits , was presented at a computer graphics and interactive techniques conference in Vancouver in mid-August , and reveals a place for the program: Hollywood.

Virtually every high-end movie production contains a significant percentage of computer-generated imagery, or CGI, from Lord of the Rings to Benjamin Button," the authors write.

The production of even a short synthetic video clip costs millions in budget and multiple months of work, even for professionally trained artists, since they have to manually create and animate vast amounts of 3D content.

Thanks to AI, we can now produce the same imagery in a lot less time. And cheaper. And — if not now, soon — just as convincingly.

The process of manipulating existing video, or creating a new video with false images, as comedian Peele and others warn, can be downright dangerous in the wrong hands.

Some prominent actresses and entertainers had their faces stolen and weaved into porn videos in the most disturbing early examples of deepfakes. Using images to, as Peele warned with his Obama video, produce " fake news " is a very real possibility.

Many outlets already have taken steps to stop deepfakes.

Deepfakes Videos Video

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