Senior correspondent Alicia Acuna reports the latest from Denver. AI-generated audio that mimics humans can be so convincing that people can't tell the difference a quarter of the time – even when they're trained to identify faked voices, a new study claims. Researchers at University College London investigated how accurately humans can differentiate between AI-generated audio and organic audio, according to a report in the science and medical journal Plos One. The study comes amid the rise of deepfakes, videos and pictures that can be edited to appear as if they are actual images of other people. "Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse," researchers wrote in their paper published this month.

Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks. This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset.

FakeCatcher can detect deepfakes in real time by analysing pixels in a video to look for signs of blood flow. Intel has developed an AI that it says can detect in real time whether a video has been manipulated using deepfake technology. FakeCatcher, part of the chipmaker's responsible AI work, claims to detect deepfakes within milliseconds and with a 96pc accuracy rate. "Deepfake videos are everywhere now," said Intel scientist Ilke Demir, who designed FakeCatcher with Umur Ciftci from the State University of New York at Binghamton. "You have probably already seen them; videos of celebrities doing or saying things they never actually did."

Sarah Vitak: This is Scientific American's 60 Second Science. Early last year a TikTok of Tom Cruise doing a magic trick went viral. I mean, it's all the real thing."] Matt Groh: A deepfake is a video where an individual's face has been altered by a neural network to make an individual do or say something that the individual has not done or said. Vitak: That is Matt Groh, a Ph.D. student and researcher at the M.I.T. Media Lab. Groh: It seems like there's a lot of anxiety and a lot of worry about deepfakes and our inability to, you know, know the difference between real or fake. Vitak: But he points out that the videos posted on the Deep Tom Cruise account aren't your standard deepfakes. The creator, Chris Umé, went back and edited individual frames by hand to remove any mistakes or flaws left behind by the algorithm. It takes him about 24 hours of work for each 30-second clip. It makes the videos look eerily realistic. But without that human touch, a lot of flaws show up in ...

Deepfake images and videos pose a significant threat to biometric systems used for remote identity verification, and existing liveness technologies can detect them, making an attack vector for non-deepfakes a vulnerability businesses need to be aware of. 'Why Deepfakes aren't the Real Challenge for Remote Biometrics' was presented by Ann-Kathrin Freiberg of BioID in the latest lunch talk presented by the European Association for Biometrics (EAB). More than 250 attendees from more than 40 countries around the world pre-registered for the presentation, many of whom were highly engaged in discussion throughout. The origin of the term based on the use of deep learning to manipulate or fake an image, video or audio file was reviewed, and Freiberg shared several examples of deepfakes, including a morph fake created by a BioID employee from a free app and a single image found on the internet. Some basic tips for spotting deepfake videos were shared, such as observing the transition between different areas of the face and head, and frequency or lack of blinking.

How many times do you see a video of famous personalities saying something'strange' which you believe they have would not say? How many times you have seen some false information spread from a trusted source? Are we going to see the movie Face/Off starring Nicolas Cage & John Travolta becoming a reality? Welcome to the era of fake news/fake videos or in technical terms "Deepfake" and It's the term you are going to hear more often in the near future. Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.

This image made from video of a fake video featuring former President Barack Obama shows elements of facial mapping used for deepfakes that lets anyone make videos of real people appearing to say things they've never said. This image made from video of a fake video featuring former President Barack Obama shows elements of facial mapping used for deepfakes that lets anyone make videos of real people appearing to say things they've never said. Facebook researchers say they've developed artificial intelligence that can identify so-called "deepfakes" and track their origin by using reverse engineering. Deepfakes are altered photos, videos, and still images that use artificial intelligence to appear like the real thing. They've become increasingly realistic in recent years, making it harder to detect the real from the fake with just the naked eye.

Deepfakes, or AI-generated videos that take a person in an existing video and replace them with someone else's likeness, are multiplying at an accelerating rate. According to startup Deeptrace, the number of deepfakes on the web increased 330% from October 2019 to June 2020, reaching over 50,000 at their peak. That's troubling not only because these fakes might be used to sway opinion during an election or implicate a person in a crime, but because they've already been abused to generate pornographic material of actors and defraud a major energy producer. While much of the discussion to date around deepfakes has focused on social media, pornography, and fraud, it's worth noting that deepfakes pose a threat to people portrayed in manipulated videos and their circle of trust. As a result, deepfakes also represent an existential threat to businesses, particularly in industries that depend on digital media to make important decisions.

US Army scientists have developed a novel tool that can help soldiers detect deepfakes that pose threat to national security. The advance could lead to a mobile software that warns people when fake videos are played on the phone. Deepfakes are hyper-realistic video content made using artificial intelligence tools that falsely depicts individuals saying or doing something, explained Suya You and Shuowen (Sean) Hu from the Army Research Laboratory in the US. The growing number of these fake videos in circulation can be harmful to society – from the creation of non-consensual explicit content to doctored media by foreign adversaries that are used in disinformation campaigns. According to the scientists, while there were close to 8,000 of these deepfake video clips online at the beginning of 2019, in just about nine months, this number nearly doubled to about 15,000.

Yes, these are amazing places. I'm sure you've used one at least once. Yet, while a few types of media are clearly edited, different changes might be harder to spot. You may have heard the term "deepfake videos" recently. It originally came to fruition in 2017 to depict videos and pictures that incorporate deep learning algorithms to create videos and images that look real.