Radio Frequency Fingerprint Identification for Few-Shot Scenario via Grad-CAM Feature Augmentation and Meta-Learning

Radio Frequency Fingerprint Identification (RFFI), which leverages hardware-specific impairments in Internet of Things (IoT) devices, is widely used for device authentication and spoofing attack detection to enhance communication security. However, the existing RFFI methods heavily depend on large-scale training datasets in deep learning (DL), with severe overfitting issues if the training samples are scarce. This paper proposes a few-shot learning framework that combines feature augmentation and meta-learning to overcome these challenges. A novel data augmentation technique based on grad class activation maps (Grad-CAM) is introduced to address the scarcity of training samples, which generates augmented samples by adjusting the weights of receptive fields in feature maps, forming an auxiliary dataset for training. In meta-training, the auxiliary dataset is used to construct tasks comprising support and query sets. By extracting common features from the limited sample size, the framework trains a meta-model with robust generalization capabilities. In the deployment phase, a fine-tuning strategy further optimizes the classifier using a small labeled dataset from new IoT devices, allowing rapid adaptation with high accuracy. The proposed framework is evaluated on the large-scale open-source dataset, achieving an accuracy of 94.1% under 8-way 5-shot, with only 25 samples per device in meta-training. Meta-learning boosts performance by 15%-20%, with meta-training feature augmentation further increasing accuracy by 5%-6.6%. Compared to baseline methods, the proposed framework improves the accuracy by 30%, which outperforms the state-of-the-art algorithms by 4%-25%.