Deepfake Technology
What is the process of a deep fake?
- A person in an already-existing video or picture is replaced with another person in a deepfake type of synthetic media. Machine learning and artificial intelligence are used to modify the audio/video, which has a predisposition to deceive.
- It has attracted attention because of how easily false information, celebrity pornography, etc. may spread online.
- By adding fresh sounds or images over an existing media file, it creates a fake version of the authentic or original audio-visual data.
- The number of deep fake videos online tripled in just nine months to 15,000 in September 2019, according to AI company Deeptrace. Surprisingly, 96% of them were pornographic, and 99% of them resembled porn actresses more closely than popular women.
What role did Deep Fakes play?
- The discriminator and generator, two AI systems at odds with one another, are used to produce deep false content.
- The discriminator is accountable for determining if the multimedia information is real or fraudulent.
- When the generator and discriminator cooperate, a generative adversarial network is produced (GAN). The generator gains invaluable knowledge on how to improve the following deep fakes each time the discriminator properly decides that the material is fake.
- Establishing the intended output and producing a training dataset for the generator are the initial steps in setting up a GAN.
- After the generator starts generating output at a level that is acceptable, videos can be handed to the discriminator.
Deep Fakes technology advantages:
- GANs can be used to generate fictional medical images in order to train disease detection algorithms for rare diseases and satisfy patient privacy concerns.
- Deepfake can hasten the movement for greater equity through accessibility.
- The generator gets better at creating fake video clips as the discriminator gets better at spotting them. On the flip hand, the discriminator gets better at spotting fake videos while the generator gets better at creating them.
- Artificial intelligence will soon be able to reason more precisely as Artificial General Intelligence (AGI) advances.
- Deepfake movies can be used to enhance displays in galleries and museums.
- Deep fakes technology can be used to create AI avatars that can be used in training videos.
- Lockdowns and health issues have significantly increased the difficulty of producing videos featuring real people, which has raised interest from the corporate sector in businesses like London-based Synthesia.
- In addition to being utilised for entertainment and education, deepfake technology can be used to create customised avatars.
- Easy ways to prevent identity theft For instance, in news exposes detailing the persecution of LGBTQ individuals in Russia, AI-generated avatars have been deployed to conceal the identities of interview subjects.
- Disney has improved its visual effects by utilising high-resolution deep fakes face swapping technology as the field of deep fakes technology advances.
Issues with Deep Fake:
- Financial fraud is a problem for the entire financial system because of Deepfake.
- The security of cyber networks and the veracity of online information are both under risk in the age of fake news.
- In phishing attempts, deepfakes would make it more challenging to spot a hoax.
- Deep fakes can be used to undermine democratic processes like elections in every country.
- Given that it may be used to create fake pornographic videos and make politicians appear to say things they did not, there is a considerable chance that it could cause harm to individuals, groups, and societies.
- The public is so sceptical as a result of the abundance of convincing fakes that it is simple to dismiss any actual evidence of a crime.
- As new technology makes it possible for unskilled people to make sophisticated fakes using only a few photos, imitation movies are likely to grow more popular outside of the world of superstars. Vengeance porn will flourish as a result of this.
- The use of false identities and imposter frauds in cybercrime is on the rise.
- Issues with authenticity and respectability: It is harder to tell whether a video is real or not when blatant fakes are present.
Moving ahead:
- As media consumers, we must be able to decipher, understand, translate, and use the information we encounter.
- Meaningful regulations created in collaboration with the technology industry, civic society, and the government can aid in halting the creation and spread of hazardous deep fakes.
- Deep fakes pose risks to the government, society, economy, culture, and local communities, which should be known to policymakers.
- Before the issues caused by deep fakes can be resolved, media literacy needs to be increased.
- The only way to solve this problem is through technology solutions powered by artificial intelligence that can recognise and block deep fakes.
- Since blockchains are resistant to a number of security issues, they can be used to digitally sign and validate the authenticity of movies and documents.
Conclusion:
- Given that adversarial training (which commonly uses GANs) results in deep fakes, these fakes can be enhanced by attempting to trick an algorithmic detector and analysing the results.
- Collaborative efforts and community strategies that cross legal boundaries, platform guidelines, technical intervention, and media literacy can be efficient and ethically righteous in reducing the harm presented by malevolent deep fakes.
