deepfake - bodhak

Deepfake technology can seamlessly stitch anyone within the world into a video or photo they never actually participated in. Now, deepfake technologies new automatic computer-graphics or machine-learning systems can synthesize images and videos far more quickly.

There’s tons of confusion around the term “deepfake,” though, and computer vision and graphics researchers are united in their hatred of the word. It’s become a catchall to explain everything from state-of-the-art videos generated by AI to any image that seems potentially fraudulent.

A lot of what’s being called a deepfake simply isn’t: For instance, a controversial “crickets” video of the U.S. Democratic primary debate released by the campaign of former presidential candidate Michael Bloomberg was made with standard video editing skills. Deepfakes played no role.

How deepfakes are created

The main ingredient in deepfakes is machine learning, which has made it possible to supply deepfakes much faster at a lower cost. To form a deepfake video of somebody, a creator would first train a neural network on many hours of real video footage of the person to offer it a sensible “understanding” of what he or she seems like from many angles and under different lighting. Then they’d combine the trained network with computer-graphics techniques to superimpose a replica of the person onto a special actor.

While the addition of AI makes the method faster than it ever would are before, it still takes time for this process to yield a believable composite that places an individual into a completely fictional situation. The creator must also manually tweak many of the trained program’s parameters to avoid telltale blips and artefacts within the image. the method is hardly straightforward.

Who created deepfakes?

The most impressive deepfake examples tend to return out of university labs and therefore the startups they seed: A widely reported video showing soccer star David Beckham speaking fluently in nine languages, just one of which he actually speaks, maybe a version of code developed at the Technical University of Munich, in Germany.

And MIT researchers have released an uncanny video of former U.S. President Nixon delivering the alternate speech he had prepared for the state had Apollo 11 failed.

But these aren’t the deepfakes that have governments and academics so worried. Deepfakes don’t need to be lab-grade or high-tech to possess a destructive effect on the social fabric, as illustrated by nonconsensual pornographic deepfakes and other problematic forms.

Indeed, deepfakes get their very name from the ur-example of the genre, which was created in 2017 by a Reddit user calling himself r/deepfakes, who used Google’s open-source deep-learning library to swap porn performers’ faces for those of actresses. The codes inside DIY deepfakes found within the wild today are mostly descended from this original code—and while some could be considered entertaining thought experiments, none are often called convincing.

So why is everyone so worried? “Technology always improves. That’s just how it works. There’s no consensus within the research community about when DIY techniques will become refined enough to pose a real threat—predictions vary wildly, from 2 to 10 years. But eventually, experts concur, anyone is going to be ready to pull up an app on their smartphone and produce realistic deepfakes of anyone else.

What are deepfakes used for?

The clearest threat that deepfakes pose immediately is to women nonconsensual pornography accounts for 96 per cent of deepfakes currently deployed on the web. There is an increasing number of reports of deepfakes getting used to making fake revenge porn, says Henry Ajder, who is head of research at the detection firm Deeptrace, in Amsterdam.

But women won’t be the only targets of bullying. Deepfakes could enable bullying more generally, whether in schools or workplaces, as anyone can place people into ridiculous, dangerous, or compromising scenarios.

Corporations worry about the role deepfakes could play in supercharging scams. There are unconfirmed reports of deepfake audio getting used in CEO scams to swindle employees into sending money to fraudsters. Extortion could become a serious use case. Identity fraud was the highest worry regarding deepfakes for quite three-quarters of respondents to a cybersecurity industry poll by the biometric firm iProov. Respondents’ chief concerns were that deepfakes would be wont to make fraudulent online payments and hack into personal banking services.

For governments, the larger fear is that deepfakes pose a danger to democracy.

deepfake videos associated with the U.S. election, voting procedures, or the 2020 U.S. census.

Deepfakes Disadvantages

Rather profiting anyone, this AI-based technology has drawbacks hitting different groups of our society. Aside from creating fake news and propaganda deepfake is majorly used for revenge porn to defame the notable celebrities.

Fake videos go viral people believe originally, and keep sharing with others. This makes the targeted person become embarrassed watching such unusual acts. Until and unless an official statement of the targeted celebrity not comes, many of us start believing making, that creates their life difficult, especially once they are criticized by their fans on various platforms like social media etc.

Deepfakes Advantages

Though it’s harmful to the society it even has few advantages like creating unprecedented attention among the online audience making the web page popular on the program, as more number of individuals start searching on such erotic topics.

And few celebrities who aren’t known to everyone also become famous overnight, as people start searching reading about them, whet they are doing, what’s their profession and more about their personal background and current reputation within the market.

Another factual advantage of deepfake is, it makes us become aware of such fake things and that we shouldn’t believe everything we see around us. Once we discover that it’s fake we learn and next time when such contents come through similar sources, we take time to believe or do some research to authenticate the news.

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