  The network flow watermarking technique associates the two communicating
parties by actively modifying certain characteristics of the stream generated
by the sender so that it covertly carries some special marking information.
Some curious users communicating with the hidden server as a Tor client may
attempt de-anonymization attacks to uncover the real identity of the hidden
server by using this technique. This compromises the privacy of the anonymized
communication system. Therefore, we propose a defense scheme against flow
watermarking. The scheme is based on deep neural networks and utilizes
generative adversarial networks to convert the original Inter-Packet Delays
(IPD) into new IPDs generated by the model. We also adopt the concept of
adversarial attacks to ensure that the detector will produce an incorrect
classification when detecting these new IPDs. This approach ensures that these
IPDs are considered "clean", effectively covering the potential watermarks.
This scheme is effective against time-based flow watermarking techniques.
