RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System

• URL: http://arxiv.org/abs/2508.09186v2
• Date: Fri, 15 Aug 2025 04:36:03 GMT
• Title: RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
• Authors: Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast,
• Abstract summary: We propose RL-MoE, a novel framework that transforms sensitive visual data into privacy-preserving textual descriptions.<n> RL-MoE combines a Mixture-of-Experts (MoE) architecture for nuanced, multi-aspect scene decomposition with a Reinforcement Learning (RL) agent.<n>Our work provides a practical and scalable solution for building trustworthy AI systems in privacy-sensitive domains.
• Score: 0.9831489366502302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of AI-powered cameras in Intelligent Transportation Systems (ITS) creates a severe conflict between the need for rich visual data and the right to privacy. Existing privacy-preserving methods, such as blurring or encryption, are often insufficient due to creating an undesirable trade-off where either privacy is compromised against advanced reconstruction attacks or data utility is critically degraded. To resolve this challenge, we propose RL-MoE, a novel framework that transforms sensitive visual data into privacy-preserving textual descriptions, eliminating the need for direct image transmission. RL-MoE uniquely combines a Mixture-of-Experts (MoE) architecture for nuanced, multi-aspect scene decomposition with a Reinforcement Learning (RL) agent that optimizes the generated text for a dual objective of semantic accuracy and privacy preservation. Extensive experiments demonstrate that RL-MoE provides superior privacy protection, reducing the success rate of replay attacks to just 9.4\% on the CFP-FP dataset, while simultaneously generating richer textual content than baseline methods. Our work provides a practical and scalable solution for building trustworthy AI systems in privacy-sensitive domains, paving the way for more secure smart city and autonomous vehicle networks.

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