In 2020, it was estimated that disinformation in the form of fake news costs around $78 billion annually. But deepfakes, mainly in social media, have matured and are fueled by the sophistication of artificial intelligence are moving into the business sector. In 2019, Deeptrace, a cybersecurity company reported that the number of online deepfake videos doubled, reaching close to 15,000 in under a year. Several startups like Truepic, that's raised $26 million from M12, Microsoft's venture arm, has taken a different approach to deepfakes. They focus on identifying not what is fake, tracking the authenticity of the content at the point it is captured.
Highly realistic deepfake videos didn't quite make the splash some feared they would during the 2020 presidential election. Nevertheless, deepfakes are causing trouble--for regular people. In March, the Federal Bureau of Investigation warned that it expected fraudsters to leverage "synthetic content for cyber … operations in the next 12-18 months." In deepfake videos, which first appeared in 2017, a computer-generated face (often of a real person) is superimposed on someone else. After the swap, the fraudsters can make the target person say or do just about anything.
Apple wants to help protect children from people who use communication tools to recruit and exploit them, and limit the spread of CSAM files. On the other side, Apple's plan has been particularly controversial and has prompted concerns about the system potentially being abused by governments as a form of mass surveillance. But rather than analyzing the benefits and drawbacks of this new feature, I would like to say a few words about the cryptographic techniques and protocols used for this system implementation. Before explaining these technologies, let's step back for a moment and take a quick look at the whole process of CSAM detection and its steps to get some more context around this. NeuralHash is a perceptual hashing function that maps images to numbers. The system computes these hashes by using an embedding network to produce image descriptors and then converting those descriptors to integers using a Hyperplane LSH (Locality Sensitivity Hashing) process.
This article was originally published on our sister site, Freethink. A financial consulting firm has created AI avatars for its staff, which they can use to quickly create deepfakes of themselves for presentations, emails, and more. The challenge: During the pandemic, remote work became the norm at many companies, and meetings that might have once taken place over lunch happened over the internet instead. This transition was more difficult for some industries than others, and those that traditionally relied on face-time with clients to build relationships and secure deals may have struggled to find their footing. "[W]hile much has been written about how to collaborate remotely with coworkers … companies still are trying to figure out the best way to connect with clients over teleconferencing platforms," Snjezana Cvoro-Begovic and James Hartling, execs at the software company Cognizant Softvision, wrote in Fast Company.
Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. One of the most difficult things about detecting manipulated deepfakes and photos is that digital photo files aren't coded to be tamper evident. But researchers from New York University, as well as other researchers and start ups around the world, are starting to develop strategies that make it easier to tell if a photo has been altered, as well as finding new ways to prevent your likeness from being deepfaked, opening up a potential new front in the war on fakery. Forensic analysts have been able to identify some digital characteristics they can use to detect meddling, but these indicators don't always paint a reliable picture of whatever digital manipulations a photo has undergone. But what if that tamper-resistant seal originated from the camera that took the original photo itself?
The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In March, the FBI released a report declaring that malicious actors almost certainly will leverage "synthetic content" for cyber and foreign influence operations in the next 12-18 months. This synthetic content includes deepfakes, audio or video that is either wholly created or altered by artificial intelligence or machine learning to convincingly misrepresent someone as doing or saying something that was not actually done or said. We've all heard the story about the CEO whose voice was imitated convincingly enough to initiate a wire transfer of $243,000. Now, the constant Zoom meetings of the anywhere workforce era have created a wealth of audio and video data that can be fed into a machine learning system to create a compelling duplicate.
A financial consulting firm has created AI avatars for its staff, which they can use to quickly create deepfakes of themselves for presentations, emails, and more. The challenge: During the pandemic, remote work became the norm at many companies, and meetings that might have once taken place over lunch happened over the internet instead. This transition was more difficult for some industries than others, and those that traditionally relied on face-time with clients to build relationships and secure deals may have struggled to find their footing. "As opposed to sending an email and saying'Hey we're still on for Friday,' you can see me and hear my voice." "[W]hile much has been written about how to collaborate remotely with coworkers … companies still are trying to figure out the best way to connect with clients over teleconferencing platforms," Snjezana Cvoro-Begovic and James Hartling, execs at the software company Cognizant Softvision, wrote in Fast Company.
In this technology-driven era, it is not uncommon to see fake news and propaganda spreading like wildfire. To make matters worse, the advancement in artificial intelligence has created deepfake, a new technology emerging to be one of the most common causes for nefarious activities. Deepfake employs artificial intelligence to create fake audios, videos and pictures that seem pretty authentic. This technology is mainly used for nefarious purposes such as defamation, revenge porn, and election propaganda. In recent years, thousands of deepfake videos targeting actors, actresses and political leaders have created havoc.
The DeepFakes, which are the facial manipulation techniques, is the emerging threat to digital society. Various DeepFake detection methods and datasets are proposed for detecting such data, especially for face-swapping. However, recent researches less consider facial animation, which is also important in the DeepFake attack side. It tries to animate a face image with actions provided by a driving video, which also leads to a concern about the security of recent payment systems that reply on liveness detection to authenticate real users via recognising a sequence of user facial actions. However, our experiments show that the existed datasets are not sufficient to develop reliable detection methods. While the current liveness detector cannot defend such videos as the attack. As a response, we propose a new human face animation dataset, called DeepFake MNIST+, generated by a SOTA image animation generator. It includes 10,000 facial animation videos in ten different actions, which can spoof the recent liveness detectors. A baseline detection method and a comprehensive analysis of the method is also included in this paper. In addition, we analyze the proposed dataset's properties and reveal the difficulty and importance of detecting animation datasets under different types of motion and compression quality.
If you want to see yourself on screen with Hugh Jackman, this is your chance. The promo for Warner Bros. upcoming Reminiscence movie uses deepfake technology to turn a photo of your face -- or anybody's face, really -- into a short video sequence with the star. According to Protocol, a media startup called D-ID created the promo for the film. D-ID reportedly started out wanting to develop technology that can protect consumers against facial recognition, but then it realized that its tech could also be used to optimize deepfakes. For this particular project, the firm created a website for the experience, where you'll be asked for your name and for a photo.