Action-Oriented Synthetic Media Detection With Dynamic Probability
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Solution Overview
Problem
Existing technologies fail to provide efficient and reliable tools for detecting synthetic media objects in videos and images, particularly those that have been edited or generated to mislead viewers by adding or modifying objects that did not occur in the real world.
Innovation Solution
An action-oriented synthetic media detection system that analyzes object properties and actions within a media stream to determine the probability of synthetic media presence, using an object repository and action description repository to verify the authenticity of objects and their actions, and dynamically updates the detection probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object detection methods are used to identify synthetic media objects, then detection capability is provided, but reliability and accuracy are insufficient
Solution Approach 1:
The detection process is segmented into multiple independent analysis components: object detection, action recognition, property verification, and probability calculation. Each component handles a specific aspect of synthetic media detection, allowing the system to systematically evaluate multiple indicators rather than relying on a single detection method, thereby improving both reliability and precision.
Solution Approach 2:
The patent introduces an intermediary action recognition system that bridges object detection and synthetic media determination. By analyzing actions performed by detected objects and comparing them against physics-based expectations, the system provides an additional verification layer that enhances detection accuracy and reliability without directly modifying the core object detection process.
2Measurement precision
If comprehensive analysis of object properties and actions is performed to improve detection accuracy, then synthetic media detection reliability improves, but processing resources increase
Solution Approach 1:
The system performs partial analysis by focusing on key discriminative features rather than exhaustive analysis of all object properties. The action recognition component selectively analyzes specific actions that are most indicative of synthetic media, rather than comprehensively evaluating every possible object attribute, thereby maintaining high detection accuracy while reducing computational resource consumption.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on detection confidence levels. When initial object detection yields high confidence results, the system reduces the depth of action analysis required. Conversely, when detection confidence is low, the system intensifies analysis only for relevant parameters, optimizing the balance between detection accuracy and processing resource usage.
3Ease of operation
If static probability assessment is used, then implementation is simple, but the indicator does not dynamically reflect synthetic media likelihood
Solution Approach 1:
The system implements a feedback mechanism where detection results from object properties and action analysis are continuously fed into a probability calculation module. This module dynamically updates the synthetic media probability assessment based on accumulated evidence, creating a responsive indicator that adapts to the specific characteristics of each detected object and its actions, thereby improving reliability while maintaining operational simplicity through automated calculation.
Solution Approach 2:
The probability indicator transitions from a static value to a dynamic assessment that evolves as the system analyzes additional object properties and actions. The system continuously updates the synthetic media probability based on new information, allowing the indicator to reflect the current state of detection confidence rather than relying on predetermined thresholds, thus enhancing reliability without significantly complicating implementation.
Data Source
AI summary
A synthetic media detection system stores, for each of a set of predefined objects an object repository with previously determined characteristics of predefined objects and an action description repository with natural language descriptions of actions that are known to be possible to occur with the predefined object. The system receives at least a portion of a media stream including a video to be presented on a media player device. An object is detected in the received portion of the media stream and properties of the detected object are determined. An action is determined that is associated with the detected object. An object probability is determined that the detected object is a synthetic media object. A chronological description is determined of the actions associated with the detected object. An overall probability that the object is synthetic media object using the chronological description is determined and displayed with the video.