Generative Adversarial Networks (GANs) are at the heart of some of the most impressive recent inventions made possible by AI. GANs are helping advance fields such as entertainment, medicine and others by producing ultra-realistic images and videos and creating virtual people and advanced simulations. Even so, there is a worrying trend called deepfakes that has emerged. Because of these fake videos and images, the world faces challenges related to security, privacy and public confidence, so spotting deepfakes has become very important today.
What do Generative Adversarial Networks (GANs) refer to?
In 2014, Ian Goodfellow and his team created GANs which are designed to produce new data that looks like existing data. There are two neural networks in the architecture.
Generator – This part of the network attempts to synthesize data that seems real.
Discriminator This network studies the data to see if it is real or fake.
There is a kind of game involved, where the generator tries to fool the discriminator. The longer it continues, the better the generator becomes at generating real-looking images and the discriminator becomes better at spotting false ones. The way GANs constantly exchange information is what allows them to generate quality and realistic images.
Uses of GANs
Many fields have been revolutionized by GANs. They are employed in:
Making realistic images of people, scenes and objects.
Applying the characteristics of one image (e.g., Van Gogh’s style) to another photo.
Using medical imaging, we can improve low-resolution scans and complete any missing information in diagnostic pictures.
Gaming and AR/VR allow for the creation of fascinating worlds and lifelike characters.
At the same time, these uses of GANs show how dangerous it can be if GANs are misused to generate tricky and harmful information.
The Negative Aspects: Using GANs for Deepfakes
Creating deepfakes is considered one of the most unsettling applications of GANs. A deepfake is a kind of video, audio or image in which someone pretends to be another person and talk or act the way they did not. Deepfakes are now so lifelike with GANs that it is difficult to identify them, leading to various ethical, legal and security issues.
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Deepfakes have been applied in several situations.
Share incorrect stories and information online.
Make explicit content without the other person’s permission.
Use someone else’s identity for fraud and cybercrime purposes.
Damage people’s faith in the media and political systems.
Due to these risks, there is now global concern and as a result, new technology has been developed to spot deepfakes.
There is now an AI contest to spot Deepfakes.
With deepfakes becoming more authentic, they become more challenging to catch. For this reason, several artificial intelligence and machine learning systems now analyze and uncover media that has been altered using deepfake technology.
Several approaches are used by modern technology to detect deepfakes.
Eye, lip and face movements are examined for any small differences.
Analyzing the film scene by scene: Look for changes that seem out of place, lighting issues or any odd visual details.
Differences between lip motion and the speech picked up by the microphone.
Biometric differences: Detecting variations in images or recordings of a person’s face or voice.
To detect the most advanced deepfakes, some tools are trained using many examples of real and fake videos. Because governments, social media sites and cybersecurity firms are investing in them, deepfake detection software is becoming necessary to protect us.
Deepfakes are challenged by the use of GANs.
Surprisingly, technology for making deepfakes is also being used to catch them. Currently, researchers are using GANs to help train deepfake detection models better. Thanks to the production of many types of synthetic media, GANs assist in the creation of sizable data sets used in training detectors.
Having AI contest with AI, just as GANs do, is turning out to be an effective approach for improving deepfake technology. Furthermore, certain solutions are turning to blockchain to confirm the truthfulness of media or marking the content they produce to highlight what is real.
The Latest Trends in GANs and Catching Deepfakes
The impact of GANs will keep changing as they keep getting better and it will affect society in different ways. In addition, GANs will help create new possibilities for art and technology. On the other hand, the use of deepfakes will become more difficult to detect and handle.
In time, deepfake detection software will require the use of:
Checking videos on the spot as they are sent online or watched.
The content can be posted easily on social networks, news websites and content creator apps.
AI transparency: Tools that explain the reasons behind labeling certain content as fake or authentic.
Educating people about how to identify if media is fake.
People who work in AI, including researchers, developers and policy-makers, should find ways to ensure that new developments are responsible. GANs have made it possible for more creativity, but we must ensure that our use of them is carefully controlled.
Conclusion
Generative Adversarial Networks (GANs) allow machines to produce images and content that look extremely realistic. The development of deepfakes has shown us that the technology can also be misused. Thanks to new technologies and software for deepfake detection, there is a possible solution.