Machine Learning Detection of Embedded Video Frames in Composite Images

The patent replaces conventional mechanical/image processing detection systems with a machine learning-based system. The machine learning model is trained to recognize patterns and characteristics of embedded video frames within composite images, enabling accurate detection without relying on traditional image processing mechanisms. This substitution allows the system to achieve high detection precision for embedded frames while maintaining operational simplicity through automated learning-based detection.

The patent employs machine learning models that can adapt to various parameter changes in the embedded frames, including size reductions. The model is trained to recognize frames at different scales and resolutions, allowing it to maintain effective detection capability even when frames are significantly reduced in size within composite images. This parameter adaptation enables the system to overcome the increased detection difficulty while preserving embedding effectiveness.

The patent implements a preliminary training phase where the machine learning model is extensively trained on composite images containing embedded video frames. During this preliminary action, the model learns to recognize patterns, characteristics, and spatial relationships of embedded frames. Once trained, the model can accurately detect embedded frames in new composite images without requiring complex real-time processing, thus achieving high detection accuracy while managing model complexity through pre-computed learning parameters.