<p>Network flow fingerprinting technology extends the number of embedded bits based on watermarking, thereby conveying additional information about the marked traffic, such as the traffic origin or the identity of the marking entity. However, existing fingerprinting/watermarking techniques follow the same embedding pattern under various levels of network noise, which hinders adaptation to high-noise environments and increases the risk of information loss. Therefore, this paper introduces the concept of embedding strength and proposes a network flow fingerprinting method with adaptive embedding strength. The embedding strength is adaptive for different network flows and can be freely adjusted. To achieve this, we design a two-stage training framework to generate fingerprint delays. In the first stage, we use an autoencoder architecture to obtain the optimal embedding for the fingerprint. In the second stage, we introduce a new component-the Adaptor-to produce a minimized embedding strength that eliminates redundant embeddings from the previous stage, thus balancing robustness and invisibility. Experimental results show that, after two stages of training, our scheme achieves an extraction rate of 98.33% and a bit error rate of 0.83%. Furthermore, in high-noise environments, our scheme can adjust the embedding strength to improve the extraction rate from 60.83 to over 90%.</p>