Generative Adversarial Method Considering Communication Transmission Distortion For Neural Network Codec

Recently, induced by incorporating the ubiquitous data collected by AI application, there are more and more demand for transferring data to cloud servers due to the restriction of computing resources of devices. To transfer image data efficiently, neural network (NN) codec can be a wise choice, which can result in higher ratio of compression with similar image quality compared with conventional codec methods. However, when the NN codec is employed in the communication system, it is likely that it can be disturbed by channel distortions. In this paper, we innovatively propose a norm method to measure the NN codec's robustness for certain tasks. And with the aid of this method, we develop a greedy algorithm using gradients to find adversarial samples of certain tasks considering communication system distortions, i.e. channel distortions during compressed image or video transferring to cloud server systems. Aided by the adversarial samples, the NN codec has been proved to have better tolerance of communication distortions that the Top-1 accuracy of the image classification can be improved 1.8%.