Deepfake Technology - What Is a Deepfake | How to Fight Deepfakes | Deepfake AI | How to Spot a Deepfake

The hader video is a DeepFake prepared by an expert, in 2014 by Ian Goodfellow, a Ph.D. The student who now works at Apple. Most of the Deepfake technology is based on a generative adversarial network (GAN).


What is Deepfake?


Deepfake is an AI-based technique used to produce or alter video content so that it presents something that is not there. The term came to light as a Reddit user known as DeepFake, who in December 2017, used deep learning techniques to edit celebrities' faces on people in pornographic video clips.

Deepfake Technology - What Is a Deepfake | How to Fight Deepfakes | Deepfake AI | How to Spot a Deepfake



Deepfake video is created using two computing AI systems - one called a generator and the other as a descriptor. The generator creates a fake video clip and then asks the investigator to determine whether the clip is real or fake. Each time the discoverer correctly identifies the video clip as being fake, it gives the generator a clue what not to do when creating the next clip.

Together, the generator and the discriminator form something called a Generative adversarial network(GAN). The first step in establishing Gan is to identify the desired output and create a training dataset for the generator. Once the generator starts creating an acceptable level of output, the video clip can be fed to the discriminator.

As the generator is better at making fake video clips, the prudent get better at spotting them. Conversely, as the wiser get better at watching fake videos, the generator gets better at making them.

Until recently, changing video content in any substantial way has been more difficult. Because deepfakes are created via AI, however, they do not require the considerable skill that would otherwise take to create a realistic video. Unfortunately, this means that just about anyone can create a deeper position to promote their chosen agenda. One danger is that people will take such videos at face value; Another is that people will stop relying on the validity of any video content.


Deepfake is human image synthesis, a type of manipulation video that creates hyper-realistic, artificial representations of humans. These videos are typically produced by mixing pre-existing videos with new images, audio, and video to create the illusion of speech. This blending process is created through GAN, a class of regressive networks or machine learning systems.


"Crucially, the system is capable of introducing both the generator and the criterion parameters in a person-specific manner, so that training can be based on only a few images and done quickly so that millions of criteria need to be met, "Said the researchers behind the paper. "We show that such an approach is capable of learning highly realistic and in-person talking head models of new people and even portrait portraits."


For now, this only applies to head videos. But when 47 percent of Americans watch their news via online video content, what happens when GANs can make people dance, clap their hands or otherwise be manipulated?

However, the rate of DeepFake video has increased considerably as the distribution of DeepFake software continues. These videos are easier than creating something in Photoshop. This is because videos rely to a large extent on machine learning techniques rather than manual design skills. The software is also usually free, making it accessible to a lot of casual users. FakeApp, for example, allows users to easily create videos with face swapping features, while programs such as DeepFaceLab and FaceSwap serve as an open-source alternative.

What’s The Problem and Why Are Deepfakes Dangerous?


The most frequent result of DeepFake is their ability to question us with what we are seeing. The more DeepFake technology is available, the less we will be able to trust our eyes.

Granted, video manipulation is not new at all. People have been manipulating the video to give viewers confidence since the film's arrival. However, DeepFake introduced a new level of authenticity into the equation. It has become more difficult than ever to distinguish between these principled videos and what is real. This inability to detect deepfake will undoubtedly be used for many sinister purposes to reduce truth, justice and the fabric of our society.

If we forget the fact that more than 30 nations are actively engaged in cyberwar at any given time, the biggest concern with DeepFake may be that it is things like the un-conceived website DePunitudes, where Celebrity faces and ordinary women's faces may be based on pornography. Video content.

At the most basic level, Deepfakes are disguised to look like the truth, ”says Andrea Hickerson, director of the School of Journalism and Mass Communication at the University of South Carolina. “If we take them as truth or evidence, then we can easily draw wrong conclusions with potentially disastrous results.

With the 2020 elections and the continued threat of cyber attacks and cyberwar, we have to seriously consider some scary scenarios:


  • The armed DeepFake will be used to incite, humiliate and divide American voters in the 2020 election cycle.
  • Armed DeepFake will be used to change and influence voting behavior, but also the consumer preferences of hundreds of millions of Americans.
  • Armed Deepfake will be used to more effectively target victims in spear-phishing and other known cybersecurity attack strategies.



This means that DeepFake has put companies, individuals and the government at risk.


How to Fight Deepfakes or What’s Being Done to Fight Deepfakes?


In the current stage, it is still thankfully easy to tell when the video is darker. Slightly unnatural shaking, confusing shadows, and lack of eye blink are common signs that a video is not real. However, the Ganas are getting better with each passing day. As videos embrace more and more realism, it can be up to technical developers to build forensic detection systems. What may be needed is equivalent to finding that a picture was photo-shopped by looking at the pixels.

Thankfully, there is the talk of developing deep learning classifiers. These classifiers will inspect the raw features of the video to indicate authenticity through a biometric video watermark. But, on the other hand, GANs can, theoretically, be trained to know how to extract such forensics, according to DARPA program manager David Gunning.


The feeding algorithm hopes to help identify deepfakes and real video, computers when something is deeply fake. If this sounds like an arms race, it is because it is. Using technology to fight technology in the race ahead that eventually improved.

Probably not a solution technique. Additional recent research suggests that rats may be just the key. Researchers at the University of Oregon Institute of Neuroscience believe that "powerfully enhancing a mechanistic understanding of sound perception in a mouse model, given the powerful genetic and electrophysiological tools to examine the neural circuits available to them" Ability. "

This means that mice can inform next-generation algorithms that can detect fake video and audio. Nature can counter technology, but it is still an arms race.

While advances in deepfake technology may help deepfake spots, it may be too late. Once the trust is in technology, it is almost impossible to bring it back. If we corrupt someone's trust in the video, how long until trust is lost in news on television, in clips on the Internet, or live-streamed historical events?

While the public is sensibly asking social media companies to develop techniques to detect and prevent the spread of DeepFake, she continues, "We should also avoid setting legal rules that go too far in the opposite direction Push, and pressure platform to engage in censorship. Free expression online.

How to Spot a Deepfake?



The best way to protect yourself from a deepfake is to never take a video at face value, says Hickerson. We cannot believe that seeing is believing. Listeners should independently seek out contextual information and pay particular attention to how and why someone is sharing the video.


Generally, people are dull about what they share on social media. Even if your best friend shares it, you should think about where he got it. Who or what is the source?


The solution to this problem is to be driven by individuals until governments, technologists or companies can find a solution. If there is no immediate push for answers, however, it may be too late.


Fake News



We live in a social environment in which the task of making just one meme is enough for a person to believe in any fact, real or not. In other words, many people hold confirmation bias and will look for any facts, statistics, or hot that confirm their already held beliefs. When the video is brought into the mix, the chances of victims of confirmation bias increase.

Accordingly, this confirmation bias can easily target specific political figures, in which they can create fake footage stating that they never did, or did not act in a way that would lead to public opinion. Celebrities, foreign leaders, company faces, presidential candidates, religious personalities, and other officials/thought leaders will be bombarded with deepfake over the next four years. Also, it is up to us, the average media consumer, to understand what is real and what is fake.


Day-to-Day Actions



In the face of worrying mistrust in the media, there seems to be a lot of perception of truth in Silicon Valley. But in the case of dealing with this type of video day today, it is important to stick to a set of principals, Contains:

  • Maintain a healthy skepticism. Keep in mind that tampering in content is common, and does not immediately spread information without looking deeper.
  • Multiple source verification is important. Hold a standard of understanding for releasing a video and for what purpose. Content from one source is not verifiable as content from multiple sources.
  • Education is important. Teaching those close to you and determining and processing the credibility of information is important for every person.
  • Advocates at the state level. Journalism should use tech countermeasures for vetting purposes. Companies and government programs should invest in intensive awareness campaigns.



Understand that it will take a combination of technical defense and human defense to counter this new era of fake news. Lamps threaten the fabric of our democracy. However, understanding the risk of DeepFake in the same way that we understand the risk of ransomware or data breaches is the first step in fighting disruption.


We should all demand that platforms that promote this information be held accountable, that the government implements efforts to ensure that positive use of technology is sufficiently positive to overcome the negative, and that Ensures that we know about DeepFake and don't have enough sense to do it. Divide them.

Otherwise, we may find ourselves in a cyberwar that a hacker started nothing but an augmented video. Then what?



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