Real-time Media Alteration Using Generative Techniques

Smart overview of the Invention

The application discusses a system for real-time media alteration using generative models. It is designed to modify media content on-the-fly when a user requests to view it, ensuring the content is suitable for the viewer's preferences or restrictions. This involves detecting a trigger event when a user requests media, retrieving the original media from a server, and using an object detection model to identify parts of the media that can be altered. The system then uses a generative model to create an altered version based on user information, which is displayed instead of the original.

Background

The need for such techniques arises from the vast amount of online content, some of which may be inappropriate for certain audiences. Traditional systems often block or reject content deemed inappropriate, but this can lead to losing valuable information within the media. For instance, an identity verification image might be rejected due to inappropriate background content. The disclosed system aims to overcome these limitations by altering media instead of blocking it, thereby retaining essential information while making it appropriate for viewing.

Detailed Description

The system alters media by adding, removing, or obscuring parts of it before it is displayed to the user. For example, if a child requests to view an image of a wristwatch that originally shows an adult's watch, the system can alter the image to show a child's watch instead. Traditional systems might display unwanted content or reject images entirely due to inappropriate elements. This approach allows for retaining important content while masking inappropriate parts, ensuring successful processes like identity verification.

Implementation Process

The process begins with detecting a user request for media via a proxy server that sits between user devices and backend servers. The proxy server retrieves the original media and identifies customizable portions using classification models. An object detection model then defines bounding regions for alteration. A generative model processes these regions alongside user demographics and historical data to produce an altered version of the media. This altered version is then transmitted back to the user's device for display.

Benefits and Applications

The disclosed techniques enhance user experience by providing personalized media alterations in real-time based on individual preferences and restrictions. This personalization caters to various factors like age, content preferences, and regional restrictions, improving engagement and appropriateness of displayed media. Additionally, caching altered media for similar users reduces computational demands and speeds up access times. The system can be applied in contexts such as online shopping or streaming services where personalized content is beneficial.