Prevention of Shoulder-Surfing Attack Using Shifting Condition with the Digraph Substitution Rules

Abstract

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method). The proposed method was tested, and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password.

Image steganalysis algorithm based on deep learning and attention mechanism for computer communication

“…Traditional image steganalysis mainly relies on prior knowledge for manual operation to achieve the purpose of deciphering encrypted information. 15 The use of deep learning technology has widened in image steganography due to its growing development. Therefore, this study combines with deep learning technology proposed an ISM based on CNN and AM, hoping to promote the development of ISM.…”

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

“…Descriptors containing light and viewpoint invariance have similar matching accuracy to descriptors containing light and viewpoint variation characteristics, while descriptors containing viewpoint invariance and illumination variation characteristics have relatively low matching accuracy. When the pixel judgment threshold is within the range of, 2,5 the matching accuracy of the four combinations increases rapidly. Conversely, when the judgment threshold falls in the range of, 5,10 accuracy tends to stabilize.…”

“…l p (x hw ∶ y hw ) is the logarithmic diffusion function. The Loss function in the construction phase of the local feature descriptor is shown in Equation (5).…”

“…threshold is within the range of, 2,5 the matching accuracy of the four combinations shows a rapid increase, and it tends to stabilize when the judgment threshold is within the range of. 5,10 Figure 6 shows the accuracy recall curve. Figure 6A was tested on the HPatches Illumination dataset, while Figure 6B was tested on the HPatches View dataset.…”

“…[1][2][3] Meanwhile, computer vision has high robustness, and can be used to handle visual problems in complex environments. [4][5][6] The Sinkhorn algorithm in view of the attention graph (AG) neural network is a new DL framework, which integrates Convolutional neural network, can classify and segment images through convolutional neural network, has strong generalization ability, and can handle large amounts of data. Moreover, AG Sinkhorn does not require manual intervention when classifying and segmenting images, and the extracted feature points can be directly used for subsequent feature matching (FM).…”