Tran Van Sang

Tran Van Sang, Mhd Irvan, Rie Shigetomi Yamaguchi et al.

Natural Gradient Descent (NGD) is a second-order neural network training that preconditions the gradient descent with the inverse of the Fisher Information Matrix (FIM). Although NGD provides an efficient preconditioner, it is not practicable due to the expensive computation required when inverting the FIM. This paper proposes a new NGD variant algorithm named Component-Wise Natural Gradient Descent (CW-NGD). CW-NGD is composed of 2 steps. Similar to several existing works, the first step is to consider the FIM matrix as a block-diagonal matrix whose diagonal blocks correspond to the FIM of each layer's weights. In the second step, unique to CW-NGD, we analyze the layer's structure and further decompose the layer's FIM into smaller segments whose derivatives are approximately independent. As a result, individual layers' FIMs are approximated in a block-diagonal form that trivially supports the inversion. The segment decomposition strategy is varied by layer structure. Specifically, we analyze the dense and convolutional layers and design their decomposition strategies appropriately. In an experiment of training a network containing these 2 types of layers, we empirically prove that CW-NGD requires fewer iterations to converge compared to the state-of-the-art first-order and second-order methods.

Tran Phuong Thao, Yukiko Sawaya, Hoang-Quoc Nguyen-Son et al.

Homograph attack is a way that attackers deceive victims about which website domain name they are communicating with by exploiting the fact that many characters look alike. The attack becomes serious and is raising broad attention when recently many brand domains have been attacked such as Apple Inc., Adobe Inc., Lloyds Bank, etc. We first design a survey of human demographics, brand familiarity, and security backgrounds and apply it to 2,067 participants. We build a regression model to study which factors affect participants' ability in recognizing homograph domains. We find that for different levels of visual similarity, the participants exhibit different abilities. 13.95% of participants can recognize non-homographs while 16.60% of participants can recognize homographs whose the visual similarity with the target brand domains is under 99.9%; but when the similarity increases to 99.9%, the number of participants who can recognize homographs significantly drops down to only 0.19%; and for the homographs with 100% of visual similarity, there is no way for the participants to recognize. We also find that female participants tend to recognize homographs better the male but male participants tend to able to recognize non-homographs better than females. Security knowledge is a significant factor affecting both homographs and non-homographs; surprisingly, people who have strong security knowledge tend to be able to recognize homographs but not non-homographs. Furthermore, people who work or are educated in computer science or computer engineering do not appear as a factor affecting the ability in recognizing homographs; however, interestingly, right after they are explained about the homograph attack, people who work or are educated in computer science or computer engineering are the ones who can capture the situation the most quickly.