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

RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System 1 st Abdolazim Rezaei Department of Computer Science T exas A&M University Corpus Christi, USA 2 nd Mehdi Sookhak Department of Computer Science T exas A&M University Corpus Christi, USA 3 rd Mahboobeh Haghparast Department of Computer Science T exas A&M University Corpus Christi, USA 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. T o 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. I NTRODUCTION The growing integration of artificial intelligence (AI) and Internet of Things (IoT) technologies in intelligent transportation systems (ITS) has significantly enhanced the capabilities of urban mobility management. From traffic monitoring and congestion analysis to automated violation detection and smart infrastructure planning, ITS plays a pivotal role in shaping the future of transportation. A key component of these systems is the use of roadside cameras, which continuously capture visual data to enable real-time decision-making and improve road safety.