“Deepfake Technology: Exploring the Potential Benefits and Risks of Computer-Generated Media”
Researchers are studying future manipulation campaigns that threaten the US presidential election and have found the artificial deepfake videos produced by artificial intelligence at the forefront of these threats. Although this deepfake technology is still emerging, the potential for misuse of it causes serious consequences, and therefore technology companies and academic laboratories began to work to solve this problem. Social media platforms have developed special policies to deal with publications that include modified media to strike a balance between freedom of expression and preventing the spread of deepfake news.
In conjunction with the approaching US elections in November 2020, companies have made little progress in facing deepfake videos, but rather other difficult-to-detect fake materials are the artificial fake texts generated by artificial intelligence.
And it was announced in June 2020 that GBT-3 technology for generating texts using artificial intelligence is very similar to human texts and difficult to distinguish between them, which makes us expect a future in which most of the content on the Internet becomes the product of artificial intelligence. And if that happens, how will our interactions with this content change?
This is similar to the change that the world witnessed after the emergence of photo editing software, such as Photoshop, After Effects, etc., about three decades ago.
AI-generated media, such as deepfake videos and GBT-3 text, are different from the modified media. There is no original material to compare it with the resulting media to prove its falsehood.
Artificial intelligence texts may distort our social environment
However, discovering videos is easy compared to other modes of deepfake with some notes, like facial expressions and heartbeats that cause a slight change in a person’s color. Detection is more difficult to find in phony audio because errors are rare, and promising research efforts exist to develop means to detect it. The struggle between counterfeiters and the authorities continues.
On the other hand, celebrities and politicians may take advantage of the spread of deepfake videos to deny actual videos they were involved in by claiming that they were produced with deepfake techniques.
The artificial deepfake texts produced by artificial intelligence are a new challenge. It can be produced in large quantities in addition to the great difficulty in discovering it. Some people can use it on social media to write posts, comments, and tweets that hold the same opinions, and they produce a case, called psychology, the majority illusion, giving them the opportunity to influence people who follow the opinions of the majority.
Prepare for a new level of Deepfake
Manipulation campaigns that include spamming social media with similar real content can now be discovered. The Wall Street Journal analyzed some of these campaigns and discovered thousands of suspicious posts that included long, repeated sentences indicating that their author was one person. Therefore, the use of artificial intelligence in these campaigns will lead to different approaches in style and similar ideas, which makes discovering them difficult.
The spread of deepfake media of all kinds, such as texts, videos, and audio clips, and the difficulty of discovering them will erode people’s confidence in the content they watch. It will be completely different from the earlier people accept photo editing software like Photoshop.
One unique aspect of deepfakes is the way they use deep learning algorithms to create highly realistic, computer-generated images or videos that can be used to manipulate and deceive viewers.
Deepfake technology involves training a neural network on a large dataset of images or videos, then using that network to generate new images or videos that are similar to the original dataset. This can be used to create convincing forgeries of people’s faces, voices, and movements that can be used for various purposes, both harmless and malicious.
While there are some legitimate uses of deepfake technology, such as in the entertainment industry or for research purposes, it also has the potential to be misused for nefarious purposes, such as spreading disinformation or creating fake news. As a result, there are growing concerns about the potential ethical, social, and political implications of deepfake technology.
What is the deepfake technology exactly ?
Deepfakes are computer-generated media that use deep learning algorithms to manipulate or replace images or videos. They are created by training neural networks on large datasets of images or videos and then using that network to generate new images or videos that are similar to the original dataset.
One of the most common uses of deepfake technology is to create realistic video forgeries of people’s faces and voices. This involves training a neural network on a large dataset of images or videos of a particular person, and then using that network to generate new videos of that person saying or doing things they never actually did. This can be used for various purposes, both harmless and malicious, such as creating fake news, political propaganda, or revenge porn.
However, deepfakes can also be used for more positive purposes. For example, they can be used in the entertainment industry to create more realistic special effects or to replace actors in dangerous stunts. They can also be used for research purposes, such as creating more realistic simulations of human behavior or facial expressions.
Despite their potential benefits, deepfakes also pose significant ethical, social, and political challenges. For example, they can be used to spread disinformation and fake news, undermine trust in democratic institutions, and perpetuate harmful stereotypes and biases. As a result, there is a growing need for research, regulation, and education to address the potential risks and benefits of this technology.
here are some more details about deepfake technology:
- Deepfakes typically use a type of neural network called a generative adversarial network (GAN) to create realistic images or videos. A GAN is composed of two neural networks: a generator that creates the fake images, and a discriminator that tries to distinguish between the fake and real images. The two networks are trained together in a feedback loop until the generator can create images that are indistinguishable from real ones.
- Deepfakes are becoming increasingly realistic and difficult to detect. Early versions of deepfakes were often easy to spot because of glitches or inconsistencies in the image or video, but as the technology has advanced, the forgeries have become more convincing. This has raised concerns about the potential for deepfakes to be used to deceive people or spread disinformation.
- There are several ways to create deepfakes, including using off-the-shelf software, creating custom scripts, or using online services that automate the process. Some of these tools are freely available online, which has made it easier for non-experts to create deepfakes.
- There are also several methods for detecting deepfakes, including analyzing facial expressions, looking for inconsistencies in lighting or shadows, or using forensic techniques to analyze the digital signatures of the image or video. However, these methods are not foolproof, and it can be difficult to detect deepfakes that are highly realistic.
- The use of deepfakes has raised a number of ethical, legal, and social concerns. For example, they can be used to create fake news or propaganda, perpetuate harmful stereotypes and biases, or invade people’s privacy. As a result, there is growing interest in developing policies and regulations to address these issues, as well as in educating the public about the risks and benefits of this technology.