The Internet has millions of fake images and videos. People have been using photo editing and video editing software for years for making fake photos and videos for fun. Now when AI and ML are being used in most tasks, it is being used to create realistic fake photos and videos. You may have heard the term ‘Deepfakes.’ Deepfakes have been around for a few years. Initially, people used Deepfakes for making pornographic videos of celebrities. Now Deepfakes are being used to spread misinformation.
In 2018, we saw a video of Donald Trump giving a speech calling on Belgium to withdraw from the Paris climate agreement. This was also a deepfake. In 2019, a doctored video of U.S. House Speaker Nancy Pelosi was circulated on social media, which made her appear drunk or impaired. This was actually a fake video created using deepfake technology. There have been several other similar incidents where deepfake videos were used to spread misinformation. So, it is really important for people to know about Deepfakes. In this article, I will explain Deepfake along with a few other important things that you need to know about deepfakes.
What Is a Deepfake?
A deepfake is a type of manipulated media that involves the use of advanced artificial intelligence techniques to create realistic but false images, audio, or videos of people or events. The term “deepfake” is derived from “deep learning,” a subset of machine learning that involves training artificial neural networks to recognize and classify data.
Here’s an example video created using feepfake technology.
Deepfakes can be used for harmless fun, such as creating videos of celebrities singing or dancing. However, they can also be used for malicious purposes, such as spreading false information, defaming individuals, or creating convincing fake news. The potential for deepfakes to be used in propaganda or disinformation campaigns has raised concerns about the impact they could have on political, social, and economic stability.
Back in 2019, an AI firm Deeptrace found 15,000 deepfake videos on the Internet. It reported that 96% were pornographic. In 99% of such pornographic videos, faces from female celebrities were mapped onto porn stars. The bad thing about deepfake is that it can
How does deepfake work?
Deepfakes are created by training a machine learning algorithm on large datasets of images, videos, or audio recordings of a person. The algorithm learns to recognize patterns in the data and then uses this knowledge to generate new content that mimics the original source. The result is a convincing but fabricated piece of media that appears to show a real person saying or doing something that they never actually did.
The process of deepfake involves several things. First of all, a large amount of data is collected from the target person. Then this data is used to train the model that learns patterns and features of the target person’s face, voice, and other characteristics. Once the algorithm is trained, it creates a model of the target person and then used the model to create manipulated media of the target person. In the final stage, the output is refined using additional algorithms to make it look realistic and believable.
Best Software for making Deepfakes
There are several offline and online tools that let you make deepfakes. Some tools are basically face swap apps where people can easily identify that the video is deepfake. But some are really advanced tools that use advanced algorithms for making real-looking deepfake. There are several Deepfake software including DeepSwap.ai, FaceMagic, DeepFaceLab, MyHeritage, and Deep Video Portraits.
Dangers of Deepfake Technology?
Just look at all the popular Deepfake incidents, and you will understand the dangers of Deepfake. People are mostly using Deepfake for making pornographic content or political videos. The use of deep fake technology presents several potential dangers, including:
Spreading misinformation: Deepfakes can be used to create convincing but false videos or images of people, which can be used to spread misinformation and propaganda. This can have serious consequences, particularly in the context of politics, where deepfakes could be used to influence elections or public opinion.
Defamation and harassment: Deepfakes can be used to create false videos or images of individuals, which can be used to defame or harass them. This can be particularly damaging to individuals in positions of power or influence, who may be targeted for political or personal reasons.
Privacy violations: Deepfakes can be used to create videos or images of people without their consent, which can violate their privacy and expose them to potential harm.
Impersonation and fraud: Deepfakes can be used to impersonate individuals, which can be used for fraudulent purposes, such as accessing sensitive information or committing crimes.
Undermining trust: Deepfakes can erode trust in media and undermine the credibility of legitimate news sources, making it more difficult to distinguish between real and fake information.
Is It Possible to Detect Deepfakes?
Yes, it is possible to detect deepfakes, but it can be challenging. There are several ways to detect deepfakes. These ways are Forensic Analysis, Face and Voice Recognition, and Machine Learning Algorithms. But not all methods are foolproof. Deepfakes are becoming increasingly sophisticated and realistic. As deepfake technology evolves, detecting deepfakes will become even harder.
Researchers at the University of Albany published a study that found that the person in deepfake blinks abnormally. But recent deepfakes don’t have this problem. You need to look at the face clearly to find blurry parts in the skin, hair, or face. In most cases, the focus looks unnaturally soft.
Sensity has developed a platform that has developed Deepfake detection technology. It uses the same deep learning processes used to create fake videos. Operation Minerva also has algorithms to identify deepfakes. It promises to detect revenge porn by recognizing that the deepfake video is simply a modified version of an existing video. DeepFake-o-meter is also an open platform for DeepFake Detection.
Facebook hosted a Deepfake Detection Challenge in 2021 where it invited developers to create new technologies for detecting deepfakes and other kinds of manipulated media. The competition saw participations from more than 2000 people.
Intel has also introduced a Real-Time Deepfake Detector last year that boasts to detect fake videos with a 96% accuracy rate. It was also the world’s first real-time deepfake detector that can return results in milliseconds.
Best Ways to Prevent Deepfakes
It is really challenging to prevent deepfakes entirely, but we can follow several ways to minimize their impact and spread. We should educate people about the dangers of deepfakes and their impact. Government should also create regulations to limit the use of deepfakes for malicious purposes, such as fraud, defamation, and harassment. Software engineers should work on developing more sophisticated and accurate tools for detecting deepfakes.
