XinLi AI Blog

This blog is the reading note for the two papers: "Improving Adversarial Robustness via Guided Complement Entropy" by Hao-Yun Chen, et al., ICCV 2019 and " Adversarial Defense by Restricting the Hidden Space  of Deep Neural Networks " by Aamir Mustafa, et al. ICCV 2019. Broadly speaking, these two papers deal with the problem of adversarial attacks on natural images. Specifically, the authors of these two papers both design novel training methods that force the network to learn well-separated feature representations of different classes in some manifolds. This ensures that attackers can no longer fool the network within a restricted perturbation budget. Introduction Deep neural networks are vulnerable to adversarial attacks, which are intentionally crafted to fool them by adding imperceptibly small perturbation to an input image. Several papers have pointed out that the main reason for the existence of such perturbation is the close proximity of different class

This blog is the reading note for the paper "Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks." by He, Xiang, et al., AAAI 2019. Broadly speaking, this paper deals with the problem of adversarial attacks on biomedical image segmentation model. Specifically, it proposes a non-local context encoder which can model short- and long-range spatial dependencies and encode global contexts to enhance the adversarial robustness. Introduction Medical image segmentation is a fundamental part of image analysis for computer-aided diagnosis, which offers pixel-level annotation for ROI such as organs, lesion, structures on the medical image (e.g. MRI, CT, X-ray). However, it is challenging due to the limited number and diversity of training dataset. With the development of the hardware, CNN-based methods such as UNet, FCN have achieved great success in medical segmentation.  In parallel to this progress, recent work shows that semantic segment