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Chris Rogers
Chris Rogers

How Easy Is It To Make A Deepfake Video !!EXCLUSIVE!!

What is the simplest task that a face-swapping neural network can be asked to perform? Because it is built on autoencoders, is to reconstruct face A from face A. This might seem trivial at first, but keep in mind that even morphing between images from the same person is far from being trivial. What makes the task easier, however, is that the autoencoders can focus on the facial expressions, rather than having to re-adapt to an entirely different bone structure, skin colour, and so on. This explains why some of the most well-crafted deepfakes feature people with similar faces.

How easy is it to make a deepfake video


The longer you train a model, the better it becomes. However, the better it is, the longer it takes to make a small improvement. This is problematic, as it is not trivial to decide when to stop the training process.

Your objective is to create realistic deepfakes, not to reduce the score as much as possible. While it is true that lower scores usually produce better videos, this is not always the case. There are many operations you can do during the training process which will lower the score, although without improving the overall quality.

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These eight questions are intended to help guide people looking through DeepFakes. High-quality DeepFakes are not easy to discern, but with practice, people can build intuition for identifying what is fake and what is real. You can practice trying to detect DeepFakes at Detect Fakes.

Chesney and Citron comprehensively survey possible legislative responses to the dangers posed by this emerging technology, and their conclusions are less than encouraging. It is unlikely that an outright ban on deepfakes would pass constitutional muster. Existing bodies of civil law, such as protections against copyright infringement and defamation, are likely to be of limited utility.

It was against this backdrop that Rep. Yvette Clark (D-N.Y.) introduced the DEEPFAKES Accountability Act in June 2019. This bill would make it a crime to create and distribute a deepfake without including a digital marker of the modification and text statement acknowledging the modification. It would also give victims the right to sue the creators of these misrepresentations for damage to their reputation.

As critics point out, the broad language of the bill would make it difficult to distinguish between truly malicious deepfakes and the use of this technology for entertainment and satire, triggering First Amendment concerns. Moreover, the good guys would be more likely to add digital and verbal identifiers than would the bad actors who are trying to sow discord and swing elections.23 Prospects for enacting this bill do not appear promising.

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The use of fraud, forgery, and other forms of deception to influence politics is nothing new, of course. When the USS Maine exploded in Havana Harbor in 1898, American tabloids used misleading accounts of the incident to incite the public toward war with Spain. The anti-Semitic tract Protocols of the Elders of Zion, which described a fictional Jewish conspiracy, circulated widely during the first half of the twentieth century. More recently, technologies such as Photoshop have made doctoring images as easy as forging text. What makes deepfakes unprecedented is their combination of quality, applicability to persuasive formats such as audio and video, and resistance to detection. And as deepfake technology spreads, an ever-increasing number of actors will be able to convincingly manipulate audio and video content in a way that once was restricted to Hollywood studios or the most well-funded intelligence agencies.

Perhaps the most acute threat associated with deepfakes is the possibility that a well-timed forgery could tip an election. In May 2017, Moscow attempted something along these lines. On the eve of the French election, Russian hackers tried to undermine the presidential campaign of Emmanuel Macron by releasing a cache of stolen documents, many of them doctored. That effort failed for a number of reasons, including the relatively boring nature of the documents and the effects of a French media law that prohibits election coverage in the 44 hours immediately before a vote. But in most countries, most of the time, there is no media blackout, and the nature of deepfakes means that damaging content can be guaranteed to be salacious or worse. A convincing video in which Macron appeared to admit to corruption, released on social media only 24 hours before the election, could have spread like wildfire and proved impossible to debunk in time.

Three technological approaches deserve special attention. The first relates to forensic technology, or the detection of forgeries through technical means. Just as researchers are putting a great deal of time and effort into creating credible fakes, so, too, are they developing methods of enhanced detection. In June 2018, computer scientists at Dartmouth and the University at Albany, SUNY, announced that they had created a program that detects deepfakes by looking for abnormal patterns of eyelid movement when the subject of a video blinks. In the deepfakes arms race, however, such advances serve only to inform the next wave of innovation. In the future, GANS will be fed training videos that include examples of normal blinking. And even if extremely capable detection algorithms emerge, the speed with which deepfakes can circulate on social media will make debunking them an uphill battle. By the time the forensic alarm bell rings, the damage may already be done.

In theory, digital provenance solutions are an ideal fix. In practice, they face two big obstacles. First, they would need to be ubiquitously deployed in the vast array of devices that capture content, including laptops and smartphones. Second, their use would need to be made a precondition for uploading content to the most popular digital platforms, such as Facebook, Twitter, and YouTube. Neither condition is likely to be met. Device makers, absent some legal or regulatory obligation, will not adopt digital authentication until they know it is affordable, in demand, and unlikely to interfere with the performance of their products. And few social media platforms will want to block people from uploading unauthenticated content, especially when the first one to do so will risk losing market share to less rigorous competitors.

Another legal solution could involve incentivizing social media platforms to do more to identify and remove deepfakes or fraudulent content more generally. Under current U.S. law, the companies that own these platforms are largely immune from liability for the content they host, thanks to Section 230 of the Communications Decency Act of 1996. Congress could modify this immunity, perhaps by amending Section 230 to make companies liable for harmful and fraudulent information distributed through their platforms unless they have made reasonable efforts to detect and remove it. Other countries have used a similar approach for a different problem: in 2017, for instance, Germany passed a law imposing stiff fines on social media companies that failed to remove racist or threatening content within 24 hours of it being reported.

But although deepfakes are dangerous, they will not necessarily be disastrous. Detection will improve, prosecutors and plaintiffs will occasionally win legal victories against the creators of harmful fakes, and the major social media platforms will gradually get better at flagging and removing fraudulent content. And digital provenance solutions could, if widely adopted, provide a more durable fix at some point in the future.

In the meantime, democratic societies will have to learn resilience. On the one hand, this will mean accepting that audio and video content cannot be taken at face value; on the other, it will mean fighting the descent into a post-truth world, in which citizens retreat to their private information bubbles and regard as fact only that which flatters their own beliefs. In short, democracies will have to accept an uncomfortable truth: in order to survive the threat of deepfakes, they are going to have to learn how to live with lies.

Recently, deepfake technology has been making headlines. The latest iteration in computer imagery, deepfakes are created when artificial intelligence (AI) is programmed to replace one person's likeness with another in recorded video.

The term "deepfake" comes from the underlying technology "deep learning," which is a form of AI. Deep learning algorithms, which teach themselves how to solve problems when given large sets of data, are used to swap faces in video and digital content to make realistic-looking fake media.

There are several methods for creating deepfakes, but the most common relies on the use of deep neural networks involving autoencoders that employ a face-swapping technique. You first need a target video to use as the basis of the deepfake and then a collection of video clips of the person you want to insert in the target.

Another type of machine learning is added to the mix, known as Generative Adversarial Networks (GANs), which detects and improves any flaws in the deepfake within multiple rounds, making it harder for deepfake detectors to decode them.

GANs are also used as a popular method for creation of deepfakes, relying on the study of large amounts of data to "learn" how to develop new examples that mimic the real thing, with painfully accurate results.


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