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Synthetic Media - Deepfakes
Creation of synthetic media become easier with advancements in artificial intelligence and machine learning technologies. This also raised concerns on safety and ethical standards. In this section, we will explore one such type of technology, called deepfakes, which uses AI to manipulate contents.
What is Deepfake?
Deepfake is a synthetic media where Artificial Intelligence is used to create fake content, such as photos, videos, or audio, that closely resemble real people or events. For example, swapping faces in videos, altering lip movements to match different audio, etc. Nowadays Deepfakes are getting so perfect and hard to get identified with naked eyes.
Types of Deepfakes
- Face-Swapping Videos: This is one of the most common type of deepfakes. Here a persons face is swapped with another in a video, so that it will appear as if they said or did something they never aware of.
- Voice Cloning: This involve replicating someones voice by training on audio samples and creating synthetic speech that matches the person's tone, pitch, and speech patterns.
- Lip Syncing: This type of deepfake changes the movements of a person's lips in a video to match another audio that has been inserted. This way, it will appear that the person is saying something different from what they originally said.
How Deepfakes are Created?
Deepfake media are created using a type of machine learning algorithm called Generative Adversarial Networks (GANs). The following image shows flowchart of GAN algorithm for creating deepfake faces.
Here are step by step working of GAN neural network algorithm for faking faces.
- Analysis From Multiple Angles: When creating a deepfake, a GAN encoder analyzes photographs or videos of the target from various angles to capture details, perspectives, and patterns.
- Latent Face Representation: After analyzing GAN creates a latent face, which is a vector representation of the target's facial features.
- Two Neural Networks: GANs uses two neural networks, one will generates fake content and another that tries to detect if the content is fake. The generator network takes latent representation as input and transforms it into synthetic data. And discriminator network predict if it is real.
- Iterative Improvement: The above process is repeated, So that the generator will improve at creating more realistic content while the discriminator becomes more skilled at spotting errors. The generator uses this feedback to continuously refine its output.
Over time, the generative network improves and starts creating highly realistic media. The result is a synthetic media file that closely resembles real people and events.
Methods to Detect Deepfakes
We can detect some deepfakes by analyzing carefully through the media. Here are some tips to check originality of videos.
- Body movements against natural gravity and physics
- Unusual facial positioning
- Audio miss match or lip syncing error
- No eye blinking
- Glare on eye glasses stays at same angle even during persons movements.
These are natural ways of detecting fake contents, But nowadays technologies are advanced and deep faking technologies getting near perfect. Hence fake contents are difficult to detect for humans. Below are some technologies that can be used to detect deepfake videos.
- Blockchain Verification: Blockchain technology is used to verify the authenticity of media files by tracking their origin.
- Intel FakeCatcher: Intel's FakeCatcher is a tool that detects deepfakes by analyzing small signs in the video, like changes in blood flow in the person's face.
- AI Detection Algorithms: Advanced AI systems are trained to detect inconsistencies in deepfakes, such as unnatural movements, irregular lighting, or facial details that dont match real-world physics.
Ethical Concerns on Deepfakes
Deepfakes are raising many ethical concerns. They can be used to spread false information by making it look like people have said or done things they never aware of. This can lead to misinformation or manipulation in politics. Deepfakes can also be used for blackmailing, where someone making fake videos to threaten or force people to do things they dont want to do.