Why IT Departments Need To Consider Deepfakes

In addition to guarding against the threats posed by deepfakes, human resources employees need to be specially trained in considering the possibility of deepfakes for all employee discipline based on media that is susceptible to manipulations or deepfake technology.

Why IT Departments Need to Consider Deepfakes

Can technology really save us, as Hwang suggested, by spawning reliable detection tools? As an initial matter, we disagree with the claim that detection tools inevitably will win the day in this cat-and-mouse game. We certainly are not there yet with respect to detection of deepfakes that are created with generative adversarial networks, and it is not clear that we should be optimistic about reaching that point. Decades of experience with the arms races involving spam, malware, viruses and photo fakery have taught us that playing defense is difficult and that adversaries can be highly motivated and innovative, constantly finding ways to penetrate defenses. Even if capable detection technologies emerge, moreover, it is not assured that they will prove scaleable, diffusible and affordable to the extent needed to have a dramatic impact on the deepfake threat.

Now is not the time to sit back and claim victory over deepfakes or to suggest that concern about them is overblown. The coronavirus has underscored the deadly impact of believable falsehoods, and the election of a lifetime looms ahead. More than ever we need to trust what our eyes and ears are telling us.

While the NDAA was the first bill to become law that contains sections related to deepfakes, two further bills have each passed one Congressional chamber and remain pending in the other. (Several other bills on this topic remain under consideration in various committees.2)

The bill would also direct the Director of the National Institute of Standards and Technology (NIST) to support research to develop measurements and standards that could be used to examine deepfakes. The NIST Director would also be required to conduct outreach to stakeholders in the private, public and academic sectors on fundamental measurements and standards research related to deepfakes and consider the feasibility of an ongoing public and private sector engagement to develop voluntary standards for deepfakes.

Though in the past deepfakes appeared visibly doctored, advances in technology have made it harder to tell what is real and what is fake. As with many technologies, deepfakes have endured a maturity curve on the way to realizing their full potential. As algorithms improve, less source material is needed to produce a more accurate deepfake.

BUFFALO, N.Y. - University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes. The tool proved 94% effective in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing to be held in June in Toronto, Canada."The cornea is almost like a perfect semisphere and is very reflective," says the paper's lead author, Siwei Lyu, PhD, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering. "So, anything that is coming to the eye with a light emitting from those sources will have an image on the cornea."The two eyes should have very similar reflective patterns because they're seeing the same thing. It's something that we typically don't typically notice when we look at a face," says Lyu, a multimedia and digital forensics expert who has testified before Congress.The paper, "Exposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights," is available on the open access repository arXiv. Co-authors are Shu Hu, a third-year computer science PhD student and research assistant in the Media Forensic Lab at UB, and Yuezun Li, PhD, a former senior research scientist at UB who is now a lecturer at the Ocean University of China's Center on Artificial Intelligence. Tool maps face, examines tiny differences in eyesWhen we look at something, the image of what we see is reflected in our eyes. In a real photo or video, the reflections on the eyes would generally appear to be the same shape and color. However, most images generated by artificial intelligence - including generative adversary network (GAN) images - fail to accurately or consistently do this, possibly due to many photos combined to generate the fake image. Lyu's tool exploits this shortcoming by spotting tiny deviations in reflected light in the eyes of deepfake images.To conduct the experiments, the research team obtained real images from Flickr Faces-HQ, as well as fake images from , a repository of AI-generated faces that look lifelike but are indeed fake. All images were portrait-like (real people and fake people looking directly into the camera with good lighting) and 1,024 by 1,024 pixels.The tool works by mapping out each face. It then examines the eyes, followed by the eyeballs and lastly the light reflected in each eyeball. It compares in incredible detail potential differences in shape, light intensity and other features of the reflected light.'Deepfake-o-meter,' and commitment to fight deepfakesWhile promising, Lyu's technique has limitations. For one, you need a reflected source of light. Also, mismatched light reflections of the eyes can be fixed during editing of the image. Additionally, the technique looks only at the individual pixels reflected in the eyes - not the shape of the eye, the shapes within the eyes, or the nature of what's reflected in the eyes. Finally, the technique compares the reflections within both eyes. If the subject is missing an eye, or the eye is not visible, the technique fails.Lyu, who has researched machine learning and computer vision projects for over 20 years, previously proved that deepfake videos tend to have inconsistent or nonexistent blink rates for the video subjects.In addition to testifying before Congress, he assisted Facebook in 2020 with its deepfake detection global challenge, and he helped create the "Deepfake-o-meter," an online resource to help the average person test to see if the video they've watched is, in fact, a deepfake.He says identifying deepfakes is increasingly important, especially given the hyper-partisan world full of race-and gender-related tensions and the dangers of disinformation - particularly violence."Unfortunately, a big chunk of these kinds of fake videos were created for pornographic purposes, and that (caused) a lot of ... psychological damage to the victims," Lyu says. "There's also the potential political impact, the fake video showing politicians saying something or doing something that they're not supposed to do. That's bad."

Legislation will not be enough to contain deepfakes or shallowfakes. We also need to invest in new screening technology that can check for irregularities in visual and audio content, and to educate the public about the existence of doctored material.

Even were deepfakes (and shallowfakes) to be detected with complete accuracy, it is an open question as to whether they should always be removed from social media platforms. Facebook recently refused to take down a number of doctored video clips, including of Nancy Pelosi and Mark Zuckerberg, which it said did not violate its moderation rules. This points to the challenge of distinguishing malicious attempts to spread disinformation from harmless satire. Still, even if their findings are not always acted on, detection tools should at least be accurate and accessible to those who need them. Journalists in particular could benefit from screening aids as they attempt to discern fact from fiction in the content they view. The Wall Street Journal has reportedly formed a 20-person strong Media Forensics Committee to advise its reporters on how to spot doctored video footage, and has invited academics to give talks on the latest innovations in deepfake screening.

Whether it is establishing new regulations, enhancing screening methods or raising public awareness, there are many ways to strengthen the oversight of deepfakes and shallowfakes. However, there is still much that is unknown about the effectiveness of different interventions, including their potential for unintended consequences. The government, tech companies and media forensics specialists must continue exploring and piloting new containment measures, while being mindful not to squeeze out beneficial uses of audio and visual manipulation. They will also need to do so at a pace that matches the speed at which the underlying technology progresses.

Myth 3: Anyone can make sophisticated deepfakes that pass the bar of believabilityReality: While supportive software like FakeApp has allowed more people to try their hand at deepfakes, high quality audio and visual synthesis still requires considerable expertise.

Many deepfakes concerning an individual make their way to exploitive and pornographic websites, affecting people unaware of the situation. With the AI technology used for deepfakes becoming increasingly intelligent every year, fewer source data is needed to create a clear image or recording of a victim.

At present, deepfakes already pose a serious threat to individual and organizational reputations. However, as the technology to create them will become cheaper and even more sophisticated in the future, the problem is only likely to worsen. We believe that companies should consider this emerging possibility of synthetic media attacks and act now. Failing to do so may leave them exposed to the risk of losing irretrievable corporate reputation.

If a deepfake is used for criminal purposes, then criminal laws will apply. For example, if a deepfake is used to pressure someone to pay money to have it suppressed or destroyed, extortion laws would apply. And for any situations in which deepfakes were used to harass, harassment laws apply. There is no need to make new, specific laws about deepfakes in either of these situations. 041b061a72