Synthetic media is a name for digital content (images, videos, audio, or text) that is artificially created or manipulated using AI and machine learning technologies, rather than recorded from real-world events.
This includes deepfakes, AI-generated art, synthetic voices, and computer-generated imagery.
A simple form would be using a photo filter that ages your face or swaps your gender on social media apps. More complex examples include AI tools like DALL-E that can create entirely new images from text descriptions, or deepfake technology like AKOOL that can make it appear as if a public figure is saying something they never actually said.
Truth battles artifice as synthetic media reshapes our world. Creators build AI tools to generate fake images, videos, and voices. Critics fear deception, while artists see liberation.
The market demands innovation, yet society craves authenticity. Teams clash over ethical limits, while individual creators feel pressure to keep pace. Who controls these tools? When does creativity become manipulation?
In this article, we will cover it all.
How Does Synthetic Media Work
Synthetic media works by using advanced AI algorithms, such as deep learning and neural networks, to analyze vast amounts of data and generate new content based on patterns and characteristics of the original input.
The process typically involves the following steps:
- Data collection: Large datasets of images, videos, audio, or text are gathered to train the AI models.
- Data processing: The collected data is cleaned, formatted, and labeled to ensure optimal performance during the training process.
- Model training: The AI algorithms are exposed to the preprocessed data, to learn and recognize patterns, styles, and features specific to the content type.
- Content generation: Once trained, the AI models can generate new content by combining and manipulating the learned patterns and characteristics. This can involve creating entirely new content or modifying existing content.
- Refinement and optimization: The generated content is fine-tuned and optimized based on user feedback, additional data inputs, and specific goals or constraints.
Non-synthetic media is content captured directly from reality, like a photo taken on your phone or a video recorded at an event. Synthetic media is artificially created or manipulated using AI and machine learning, but based on patterns data from non-synthetic media.
Examples of synthetic media include:
Here are some real world success stories:
How Synthetic Media Connects to Deepfakes
One of the most well-known, modern applications of synthetic media is deepfake technology. Deepfakes use advanced AI techniques to create convincing videos of people saying or doing things they never actually said or did.
The process involves training an AI model on a large dataset of images or videos of a specific person, allowing it to learn and replicate their facial features, expressions, and movements with a high degree of accuracy.
Today, they are called AI avatars more often.
To create a deepfake, the target person's face is extracted from the training data using computer vision techniques. The extracted faces are then aligned and encoded into a compact representation that captures the essential characteristics of the person's appearance.
The encoded facial features are superimposed onto a destination video, replacing the original person's face with the target person's face. Complex blending techniques ensure the end result is top-notch.
Deepfakes have gained significant attention in recent years due to misuse, such as spreading disinformation, engaging in fraud, or harassing individuals. In 2024 the biggest deepfake misuse was with Taylor Swift's images.
As the technology behind deepfakes continues to improve, it becomes increasingly difficult for viewers to distinguish between authentic and fabricated content.
At the same time, deepfakes can also be used for positive purposes, such as creating entertaining content, improving educational experiences, or even assisting in medical research.
Types of Synthetic Media
Synthetic media covers a wide range of AI-generated content, including text, audio, video, and images. Let’s see the list so far:
- Text-based synthetic media
Text-based synthetic media involves the use of AI algorithms, such as GPT (Generative Pre-trained Transformer), to generate human-like text. These algorithms are trained on vast amounts of textual data, to understand and replicate patterns, styles, and semantics. The use cases for text-based synthetic media are:
- AI-powered chatbots and virtual assistants
- Automated content creation for news articles, product descriptions, and social media posts
- Language translation and summarization
Top positive applications of text-based synthetic media are:
- Assisting people with disabilities in writing by helping them express their thoughts more fluently through AI-powered text completion and refinement
- Rapid translation and localization of content across languages while maintaining natural-sounding text
- Helping students and researchers brainstorm ideas and generate initial drafts to overcome writer's block
Audio-based synthetic mediaAudio-based synthetic media is a term for generating or manipulating speech and other sounds using AI. By training on large datasets of human speech and audio recordings, AI models can create realistic and convincing audio content. Some examples of audio-based synthetic media include:
- Text-to-speech systems that generate human-like speech from written text
- Voice cloning and voice deepfakes to replicate a person's voice
- AI-generated music and sound effects
Top positive applications of audio-based synthetic media are:
- Enables efficient production of audiobooks and educational content in multiple languages without re-recording
- Helps companies create consistent customer service voices across different languages and platforms
Video-based synthetic mediaVideo-based synthetic media involves the creation and manipulation of video content using AI algorithms. This type of synthetic media has gained significant attention due to the rise of deepfakes, which are highly realistic videos that depict people saying or doing things they never actually said or did. Other applications of video-based synthetic media include:
- Virtual avatars and digital humans for entertainment, education, and customer service
- Video synthesis and animation, that enable the creation of realistic video content without the need for physical actors or sets
- Video enhancement and restoration, improving the quality of existing video footage
Top positive applications of video-based synthetic media are:
- Educational content showing historical events or scientific concepts that would be impossible to film
- Generating background scenes and special effects digitally for movies to cut down costs
- Accessible training videos in multiple languages by synchronizing lip movements with translated audio
Image-based synthetic media
Image-based synthetic media focuses on generating, manipulating, and enhancing images using AI algorithms. By training on large datasets of images, AI models can create highly realistic and detailed images that mimic real-world content. Some examples of image-based synthetic media include:
- AI-generated art and designs, such as those created by DALL-E, Midjourney, or Stable Diffusion
- Synthetic images of people, objects, and scenes that are indistinguishable from real photographs
- Image manipulation and editing, such as removing objects, changing backgrounds, or adjusting facial features
Top positive applications of image-based synthetic media are:
- Helps architects and designers visualize projects before construction by generating photorealistic renderings
- Enables artists to quickly prototype different creative concepts before committing to final artwork
- Assists law enforcement in aging missing person photos or generating suspect composites based on descriptions
Advantages of Synthetic Media
Let's explore some of the key advantages of synthetic media:
Faster Content Creation
Synthetic media allows for the rapid and cost-effective creation of high-quality content. With AI algorithms to generate text, audio, video, and images, businesses and individuals can save time and resources compared to traditional content production methods.
This is particularly useful for industries such as advertising, marketing, and entertainment, where the demand for fresh and engaging content is constant.
Accessibility and Localization
Synthetic media can help make content more accessible to a wider audience. Text-to-speech systems and AI-generated subtitles can assist individuals with visual or hearing impairments, while AI-powered language translation can help businesses reach global audiences more effectively.
Additionally, synthetic media can be used to create localized content, adapting to different languages, cultures, and preferences.
Risk-free Scenario Training
Medical students can practice diagnosing rare conditions using AI-generated images of symptoms that would be difficult to document in real patients.
For example, a teaching hospital could generate thousands of variations of melanoma presentations across different skin types and stages, so students can develop pattern recognition skills without waiting years to encounter these cases naturally.
Similarly, emergency response teams can train using synthetic videos of disaster scenarios that would be dangerous or impossible to film, like realistic nuclear accident simulations or various types of structural collapses.
Disadvantages of Synthetic Media
While synthetic media offers numerous advantages, it is equally important to acknowledge and address the potential disadvantages and risks associated with this technology.
As synthetic media becomes more widespread, we need to consider the ethical, social, and legal implications of its use.
Spread of Disinformation and Fake News
One of the most significant risks associated with synthetic media is its potential to spread disinformation and fake news.
As AI-generated content becomes more realistic and difficult to distinguish from authentic content, it can be used to create and disseminate misleading or false information.
This can have serious consequences, such as influencing public opinion, undermining trust in media and institutions, or even inciting violence.
Privacy and Security Concerns
Synthetic media raises significant privacy and security concerns, particularly when it comes to the use of personal data.
AI algorithms require large amounts of data to create realistic synthetic content, and this data may include personal information, such as images, voice recordings, or biometric data.
If this data is not properly secured or if individuals do not provide explicit consent for its use, it can lead to privacy violations and potential misuse.
The Future of Synthetic Media
Market data shows synthetic media exploding through 2030. Real-time AI generation will match human-created quality, as new systems will combine text, voice, and video simultaneously.
By 2027, we predict 90% of online content will contain synthetic elements, and tech giants are already building detection tools and safety frameworks. Authentication systems and watermarks will become standard.
“Synthetic reality” platforms will arise where users seamlessly interact with AI-generated environments and characters.
It is clear that synthetic media has positive implications if used correctly, and creating quality content has never been simpler.
Solutions like AKOOL’s Talking Photo enable users to bring still images to life by animating facial expressions and lip movements. With Face Swap users can swap faces in videos or images seamlessly.
If you’ve found the use cases and example above interesting, try our Face Swap, Talking Photo, Talking Avatar, or Streaming Avatar tools to explore the synthetic images and deepfake by yourself.
