Deepfakes are synthetic media, usually videos, created with deep learning technology. By manipulating images, videos, and voices of real people, a deepfake can portray someone doing things they never did, or saying things they never said.
By feeding a machine learning model thousands of target images, a deepfake algorithm can learn the details of a person’s face. With enough training data, the algorithm can then predict what that person’s face would look like when mimicking the expressions of someone else. A similar process is used for training deepfake algorithms to mimic the accent, intonation, and tone of a person’s voice.
The Public Response to Deepfakes
The start of 2020 came with an interesting shift in response to deepfake technology, when Facebook announced a ban on manipulated videos and images on their platforms. Facebook said it would remove AI-edited content that was likely to mislead people, but added that the ban doesn’t include parody or satire. Lawmakers, however, are skeptical as to whether the ban goes far enough to address the root problem: the ongoing spread of disinformation.
The speed and ease with which a deepfake can be made and deployed, as shown in this article by Ars Technica, have many worried about misuse in the near future, especially with an election on the horizon for the U.S. Many in America, including military leaders, have also weighed in with worries about the speed and ease with which the tech can be used. These concerns are heightened by the knowledge that deepfake technology is improving and becoming more accessible.
This news is the latest in a series of initiatives to detect and regulate deepfake releases. Exactly how to handle them is an ongoing discussion. Twitter announced in November that it intended to draft a deepfakes policy. In December, Facebook announced The Deepfake Detection Challenge in conjunction with tech giants like Microsoft and Amazon. The challenge offers financial rewards for the building of technology that helps to detect manipulated media. Google, too, has contributed a dataset of deepfakes for the purposes of developing better technology to detect them.
The Year of Consumerized Deepfakes?
This growing concern around deepfake detection has not stopped its move into social media. In fact, recent technological developments on social media platforms have people talking about the idea of consumerized deepfakes. Code found in mobile apps Douyin and TikTok has revealed technology allowing users to insert their face into videos starring other people. Though the application is still unreleased, it’s a prime example of how social media platforms are utilizing deepfake technology.
Snap, the company behind Snapchat, has also reportedly acquired AI Factory, an image and video recognition start-up. Reports state that Snap used AI Factory’s technology for a new face swapping feature, raising some concerns around the possibility of deepfake usage. These applications of deepfake technology will likely continue as social media apps look to entice new users.
However, together with worries surrounding the misuse of this technology, deepfake detection is also improving. Teams at Microsoft Research and Peking University recently proposed Face X-Ray, a tool that recognizes whether a headshot is fake. The tool detects points in a headshot where one image has been blended with another to create a forgery. Though the technology is a step in the right direction, the researchers also state their technology is unable to detect a wholly synthetic image. This would make it weak against adversarial samples.
However, most researchers are pointing to improved education as the best mode of defense. They recommend keeping in mind the following questions when viewing video content:
- Is the video bizarre or exceptional?
- Is the video quality low or grainy?
- How short is the video? (30 to 60 seconds long is common for current deepfakes.)
- Is the content visually or aurally strange? (blurry faces, strange lighting, voices/lips out of sync, etc.)
The above news articles point to a clear trend: deepfake technology isn’t going away. Its use in the future will range from benign and novel to potentially destructive and damaging. Development and detection is quickly becoming a race as detection methods struggle to keep up with improving technology. The result? Regular consumers of news media will find it more difficult to differentiate the real news from the fake. This makes a strong case for improved education, which means focusing on how to educate, and where to start.
With this in mind, how tech companies like Twitter and Facebook regulate deepfake technology will have a huge impact on how people consume and understand it. 2020 will likely bring more discussion around regulation, and more development for entertainment and social media. This will go hand in hand with improved technology for both implementation and detection. But for a deeper look at the potential threats and countermeasures to deepfake technology, see our comprehensive article here.
Deepfakes is one of many machine learning trends we expect to see in the news in 2020. Read our report on the state of facial recognition bias here.