Machine Learning-Assisted Watermarking via Bias Control of Electro-Optical Modulator

We propose an optical watermarking strategy based on precise bias tuning of an electro-optical modulator and a convolutional neural network-enabled decoder. This scheme can be used for identity authentication at the physical layer of optical networks. This method is demonstrated with a transmission demo of about 250 kbit/s watermarking data in a 10-Gbit/s public 20-km communication. The watermarking data is modulated on the bias states of the modulator based on the precise and stable bias control technique. The watermark embedding has been verified to make almost no impact on the 20-km public transmission. For the legal receiver, the invisible watermark data can be extracted with the aid of the convolutional neural network-enabled decoder to verify the legality of the received data. Only one optical source is needed in the proposed scheme and the watermarking is integrated into the public signal naturally. We analyzed and verified the influence of watermarking data on the bit error rate and the channel capacity of the public data. The privacy and security of watermarking data are also analyzed.