Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Recent research has intensively revealed the vulnerability of deep neural networks, especially for convolutional neural networks (CNNs) on the task of image recognition, through creating adversarial samples which '"slightly" differ from legitimate samples. This vulnerability indicates that these powerful models are sensitive to specific perturbations and cannot filter out these adversarial perturbations. In this work, we propose a quantization-based method which enables a CNN to filter out adversarial perturbations effectively. Notably, different from prior work on input quantization, we apply the quantization in the intermediate layers of a CNN. Our approach is naturally aligned with the clustering of the coarse-grained semantic information learned by a CNN. Furthermore, to compensate for the loss of information which is inevitably caused by the quantization, we propose the multi-head quantization, where we project data points to different sub-spaces and perform quantization within each sub-space. We enclose our design in a quantization layer named as the Q-Layer. The results obtained on MNIST and Fashion-MNSIT datasets demonstrate that only adding one Q-Layer into a CNN could significantly improve its robustness against both white-box and black-box attacks.
Output:
We propose a quantization-based method which regularizes a CNN's learned representations to be automatically aligned with trainable concept matrix hence effectively filtering out adversarial perturbations.