Reinforcement Learning Audiovisual Sanitization

The patent implements a reinforcement learning framework where the masking algorithm receives feedback through a reward function that quantifies privacy protection effectiveness. The system continuously learns from the feedback signal, adjusting masking strategies to maximize privacy protection while minimizing information loss. The reward function provides explicit feedback on whether sensitive information was successfully masked, enabling iterative improvement of the sanitization process.

The patent replaces traditional mechanical/manual video sanitization techniques with an intelligent system based on deep learning and reinforcement learning. Instead of simple obfuscation algorithms, the system uses neural networks to identify sensitive information and reinforcement learning to optimize masking decisions, substituting rule-based mechanical processes with adaptive intelligent processing.

The patent performs preliminary actions by pre-processing the video content to identify potential sensitive regions before applying the full reinforcement learning masking process. The system prepares the environment and initial state representations in advance, reducing the computational burden during the actual masking optimization phase. This preliminary identification step simplifies the subsequent reinforcement learning task.

The patent segments the video processing task into distinct components: initial sensitive region detection, reinforcement learning policy optimization, and final masking application. By dividing the complex processing into manageable segments, the system reduces overall computational complexity while maintaining effective privacy protection. Each segment can be optimized independently and processed efficiently.

The system performs preliminary identification of sensitive information using pre-trained detection models before applying the reinforcement learning masking process. This advance identification allows the system to prepare masking strategies in advance, reducing the time required for real-time processing while maintaining effective privacy preservation.

The patent applies partial masking actions by focusing computational resources on identifying and masking only the most critical sensitive regions rather than processing every frame uniformly. The reinforcement learning agent learns to apply masking selectively where it is most needed, improving processing efficiency while maintaining adequate privacy protection for the most sensitive content.