Website fingerprinting (WF) attacks can reveal the users' online privacy by the traffic analysis technique, even with the protection of the Tor anonymity network. Recent WF attacks tend to leverage the deep learning (DL) models, which require a large number of traffic samples for training. In this case, it is impractical for low-resource adversaries in reality. Thus, we propose a lightweight WF attack to tackle this challenge, i.e., Deep Quadruplet Fingerprinting (DQF), which only needs one training sample to obtain an accuracy of 87.1%. Regarding the overall design, DQF first combines the metric learning and meta-learning schemes. To improve the generalization ability of the trained model, DQF leverages the quadruplet networks as the architecture and modifies the quadruplet loss function. Besides, by taking the deep local fingerprinting features (DLFFs), DQF avoids losing a lot of discriminative information, which is a problem with previous attacks. To evaluate DQF, we use multiple typical datasets and conduct 11 different experiments. In closed-world settings, the accuracy of DQF can exceed the best baseline attack by 10%. In open-world settings, DQF steadily performs the best even in the most challenging scenario, namely, 1-shot learning, where previous attacks significantly degrade the performance or even fail.