A Self-Supervised Purification Mechanism for Adversarial Samples

[SmartData] B. Xie, H. Xu, Z. Xiong, Y. Li and Z. Cai, A Self-Supervised Purification Mechanism for Adversarial Samples [C]. 2022 IEEE Smart Data (SmartData), 2022: 501-509. (Best Paper Award) Download paper here

Deep learning-based techniques are broadly used in a variety of applications, which exhibit superior performance compared to traditional methods. One of mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has been increasing along with more layers and more feature maps, resulting in better image image super-resolution performance. However, this raises a problem in that all these neural networks require a significant amount of time and computational resource to train. It is not feasible to implement massive neural networks into these devices that have limited computational resources. Meanwhile, it is not a trivial thing to think about the complete model copyright protection. Therefore, there is a demand to find smaller networks that can perform well while achieving the protection of the original model’s copyright. To address this problem, this paper proposes a lightweight model to replace the original complete model for image super-resolution. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural network.