Deepfake Detectors

A deepfake is a false piece of media that has been altered or generated by deep learning algorithms. Deepfake technology has existed since the late 1990's and has been used with varying intentions in a plethora of disciplines and contexts. These include, but are not limited to, film, audio, pornography, and politics. For about twenty years after the inception of deepfake technology, it was almost exclusively used in academic contexts to learn about artificial intelligence and machine learning.

After the coining of the term "deepfake" in 2017, deepfake media became highly popularized and found itself a regular part of mainstream media. With so much attention, many began to worry about the applications of deepfake technology and looked for ways to prevent malicious uses. Many ask for technology to detect these deepfakes, but technology for detecting deepfakes has been slow to catch up with the rapidly increasing production of deepfake media.

Advantages of deepfakes

Although deep fakes are known for having harmful and negative impacts, researcher David Brumley has justified several beneficial attributes of deepfakes. He discovered that they can be used in language learning programs to generate sample sentences to help users learn different languages. It is believed that they could be used in advertising to construct narratives and produce certain positive agenda without using real people. Even though the advertisement would be fake it would be perceived as real and be effective to its viewers.

Deepfakes have been creating exciting artistic and innovative advantages in the video game, robotics, and virtual reality industries, but it has also been really helpful to the movie indsutry. Deepfake technology has helped the moive editors keep certain actors in their character roles for a cohesive storyline when they are not available. Deepfakes have been used in blockbuster film series like Star Wars and Fast and Furious, and it’s effects are being more creatively applied everyday in this industry.

The Rising Threat of Deepfakes

How do they work?

Before the threats of deepfakes are discussed, it is important to understand how they work. Deepfake videos commonly manipulate facial expressions and or swap faces. During facial manipulation, the expressions are imitated from the filmed footage into the previously existing footage, but in face swapping the face is simply transformed and morphed onto another body from previously existing footage. The technology mobilizes machine learning techniques and then gives the computer real data about the image or video so it can create the fake version of it. The algorithms that feed image data into a computer use spatial and material information to learn how to map facial movements from on person or image to another.

Rising Threats

Deepfakes started becoming easier to create for more people. While it is unclear the original reason deepfakes were invented, one of its first uses was in overdubbing video to lip-sync with a speaker. People began making fake pornography of celebrities and notable personalities.[needs citation(s)] Politicians were made to make politically incorrect comments, and they began to worry about their campaigns and credibility.[needs citation(s)] In response to these worries, many companies, such as Facebook and Twitter, banned and made an effort to remove deepfakes from their platforms. Deepfakes are still relatively uncommon[needs citation], but as they become more abundant, deepfake detection will become increasingly important.

As deepfakes become more prevalent, there is worry that the deepfake detectors won't help the people who need protection the most.[needs citation] LGBTQ+ individuals are at a larger risk for hate crimes from deepfakes.[needs citation] Scientists say that the most worrisome risk is not that large celebrities will be targeted or elections swayed, but rather that private citizens will be targeted and won't have the funds or resources to clear their name. By imitating a CEO asking employees to send money, corporate scams also pose a threat. Identity theft using deepfake technology can be used to commit financial fraud and other crimes.

Deepfakes in Democratic Processes

The mass propagation of deepfakes cause the distortion of democratic discourse and eroding of trust in institutions, both of which are highly relevant to democratic elections. Deepfakes are a form of disinformation; however, the law with regard to campaign speech is not very helpful in addressing the threat of deep fakes because election law is shaped by a compelling concern for the protection of first amendment rights. This is especially true when it comes to deepfake parodies for they are generally seen as a form of free expression. The law favors false campaign speech over violations of free speech for fear that regulating campaign speech would become political. Despite the law, however, the harm that deepfakes cause in undermining trust in electoral outcomes stands. Deepfakes along with other kinds of false speech distort campaign results and threaten public trust in those results.

Former president Trump retweeted this video of Nancy Pelosi, Speaker of the House of Representatives, in May of 2019; it had been altered to supposedly provide evidence of health problems.

For instance, deepfakes are used to deter voters from voting by means of a threat to release deepfaked pornographic images. Even if it was unclear how many people had been deterred, once voters become aware of the tactic, trust in the integrity of election results may be eroded. Another scenario is when a deepfake is used to instill mistrust by falsely suggesting that a candidate cheated in a public debate, thereby calling into question the legitimacy of the political process. Overall, reputational harm and misattribution distort how voters perceive and understand candidates and, even if an individual viewer is aware of the manipulations, he or she may believe that others are not, which could further degrade their trust in the ability of others to make well-informed voting decisions. With new uncertainties injected into the question of whether voters think their peers are well-informed, trust in democratic processes is undermined.

Deepfake Detectors

As deepfakes started becoming more prominent and more people started to be able to make them, it became increasingly important to figure out a way to detect a deepfake video or image. It quickly became a race between tech companies to make the first accurate deepfake detector. Facebook, Microsoft, and Google were quick to jump into research. Researchers from the USC Information Sciences Institute developed a new tool that focuses on subtle face and head movements to detect a deepfake video with 96% accuracy. Microsoft starting using a video authenticator technology that finds things like greyscale pixels at the boundaries of where the deepfaked face was pasted onto the original face. Microsoft ran a lot of data (including some from a Facebook face-swap database) to create the video authenticator. Some technologies use what researchers call a soft biometric signature , which is basically the mannerisms and speech patterns of a certain person, to show that a video is not actually that person. It is really hard to mimic the mannerisms and facial expressions of a person digitally so using the soft biometric signature is very accurate right now with a detection rate of about 92%. Deepfake detection is still fairly new and as deepfakes rise in popularity the need for accurate deepfake detectors will rise as well.

Shortcomings in Deepfake Detection

While there has been tremendous advancement in detecting deepfake media, this detection technology remains far behind the technology used to create the deepfakes. Recognizing this, some companies that produce deepfake technology have issued ethics statements in order to be more transparent about the potential for misuse of their technology. In one of these ethics statements, deepfake technology company Descript admits that "the quality of generative media could increase at a rate that outpaces technology designed to detect it." This would present a deficit for deepfake detection technology. Along those lines, Descript admits that despite all the research underway to help detect deepfake media, it remains unclear how this issue will be resolved.

In order to minimize the dissemination and impact of deepfake media on society and individuals, consumers are urged to be critical of every piece of media they consume and to verify any information they find with other reliable sources before sharing with others.