Fake Images, videos like Original created by AI.. Even can't prove fake ...

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🌅 India Rising

The creation of fake videos using AI technology typically involves "deepfake" technology. Deepfakes are videos that use machine learning models to manipulate or generate visual and audio content with a high potential to deceive. The term itself is a blend of "deep learning" and "fake," indicating that deep learning algorithms are used to create these fakes.

Here’s a basic overview of how deepfakes are made:

1. Data Collection: A large amount of video and audio data of the target person is collected. This data helps in training the model to understand how the person looks and speaks from different angles and under various lighting conditions.

2. Model Training: Two competing AI models are typically used. One is a generator that creates images and sounds that mimic the target, and the other is a discriminator that tries to detect whether the outputs are real or fake. Over time, through a process known as adversarial training, both models improve, with the generator producing increasingly convincing fakes.

3. Refinement: The created video might go through further refinements to improve realism, such as adjusting lip-syncing, facial expressions, and eye movements to perfectly align with the audio.

4. Rendering and Compositing: The final step involves rendering the video outputs and integrating them smoothly so that the transitions and overall look feel seamless.

Deepfakes can be used for various purposes, from humorous content to more malicious intents such as spreading misinformation, impersonating public figures, or creating non-consensual adult content. The increasing sophistication of these technologies makes it increasingly difficult to distinguish between real and fake content, raising ethical and legal concerns.

To combat the risks associated with deepfakes, researchers are working on developing detection techniques that can identify these AI-generated fakes, and there is growing discussion about the need for legal and regulatory frameworks to manage the potential harms associated with this technology.

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