A Novel Robustness-Enhancing Adversarial Defense Approach to AI-Powered Sea State Estimation for Autonomous Marine Vessels
针对自主海洋船舶的海况估计模型易受恶意数据攻击的问题，提出SecureSSE防御策略，通过多尺度特征提取、特征卷积聚合和扰动样本训练三个模块显著提升模型鲁棒性，实验验证了其有效性。
Sea state information is significant for the guide of maritime activities of autonomous vessels. The sea state estimation (SSE) model, powered by artificial intelligence (AI), has shown great effectiveness but is susceptible to malicious data attacks. These attacks can lead to significant declines in the system’s performance and result in incorrect predictions about the sea state. This study introduces SecureSSE, a strategy for protecting SSE models in autonomous marine vessels from adversarial attacks. This approach incorporates three main components: 1) the multiscale feature extraction learning (MFEL) module; 2) the feature convolution aggregation learning (FCAL) module; and 3) the perturbation examples training (PET) module. The PET module is specifically crafted to create perturbation examples that are in line with unaltered data, leveraging the capabilities of both the MFEL and FCAL modules to efficiently extract and integrate detailed features from ship motion data. Our proposed SecureSSE approach is shown to significantly improve the resilience of deep learning models against potential attacks. Through experimental testing, we have validated the effectiveness of this method in enhancing SSE. Additional ablation studies highlight the critical role of each module within the SecureSSE framework. To our knowledge, this is the first study to address adversarial attacks in this context and to propose a comprehensive defense mechanism for SSE systems in autonomous marine vessels.