Papers - YAMABA Hisaaki
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Aridome N., Katayama T., Kita Y., Yamaba H., Aburada K., Okazaki N.
Proceedings of International Conference on Artificial Life and Robotics 575 - 579 2025.2
Language：English Publishing type：Research paper (international conference proceedings) Publisher：Proceedings of International Conference on Artificial Life and Robotics
As a method for detecting layout defects in web pages, image-based visual regression testing is proposed. However, it has the problem that it takes time to detect unintended layout differences that are not based on HTML code changes. This paper proposes a prototype of MixVRT which is a tool to detect layout defects in web pages. It is a visual regression testing tool that highlights layout defects in web pages. From evaluation experiments, the time find to layout defects can be reduced.
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Kimura Y., Katayama T., Kita Y., Yamaba H., Aburada K., Okazaki N.
Proceedings of International Conference on Artificial Life and Robotics 580 - 584 2025.2
Language：English Publishing type：Research paper (international conference proceedings) Publisher：Proceedings of International Conference on Artificial Life and Robotics
The digitalization of forms is being promoted. One of the effective ways to manage contents filled in fields is using electronic forms. Several tools have been developed to generate them automatically. However, when you use a paper form, the layout of the original form may change, and it takes time to generate electronic one because it is necessary to place fill-in fields on an electronic form by dragging with a mouse. This paper proposes a method for automatic fill-in fields detection and labels assignment to reduce time required to place fill-in fields without changing the layout. The proposed method can reduce the time to place fill-in fields.
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A study of Collaborative malware detection using item response theory Reviewed
Takuro Inada, Shotaro Usuzaki, Kentaro Aburada, Hisaaki Yamaba, Tetsuro Katayama, Mirang Park, Naonobu Okazaki
Proc. 2025 Int'l Symp. on Artificial Life and Robotics (ISAROB 2025) 1070 - 1073 2025.1
Publishing type：Research paper (international conference proceedings)
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Introducing Generative AI into Unrealistic Image CAPTCHA for Effective Image Generation Reviewed
Kana Saiki, Hisaaki Yamaba, Shotaro Usuzaki, Kentaro Aburada, Masayuki Mukunoki, Mirang Park, Naonobu Okazaki
Proc. 2025 Int'l Symp. on Artificial Life and Robotics (ISAROB 2025) 1085 - 1090 2025.1
Authorship：Corresponding author Publishing type：Research paper (international conference proceedings)
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Introduction of Feature Points in Images for Improvement of Finding Letters Type CAPTCHA Based on Neural Style Transfer Reviewed
Ramu Kiura, Hisaaki Yamaba, Shotaro Usuzaki, Kentaro Aburada, Masayuki Mukunoki, Mirang Park, Naonobu Okazaki
Proc. 2025 Int'l Symp. on Artificial Life and Robotics (ISAROB 2025) 1079 - 1084 2025.1
Authorship：Corresponding author Publishing type：Research paper (international conference proceedings)
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Investigation of Detection Methods for Trojaned DNNs under Specific Conditions Reviewed
Shunya Izaki, Shotaro Usuzaki, Kentaro Aburada, Hisaaki Yamaba, Tetsuro Katayama, Mirang Park, Naonobu Okazaki
Proc. 2025 Int'l Symp. on Artificial Life and Robotics (ISAROB 2025) 1074 - 1078 2025.1
Publishing type：Research paper (international conference proceedings)
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ASLA: Automatic Segmentation and Labeling by Deep Learning for Document Pictures Reviewed
Kakinoki Kanta, Katayama Tetsuro, Kita Yoshihiro, Yamaba Hisaaki, Aburada Kentaro, Okazaki Naonobu
Journal of Robotics, Networking and Artificial Life 10 ( 4 ) 362 - 367 2024.9
Language：English Publishing type：Research paper (scientific journal) Publisher：ALife Robotics Corporation Ltd.
In this paper, we propose ASLA, a segmentation and label generation system for document pictures. ASLA reduces the duration needed for separating document pictures into areas and label generation. By using the application example, we have verified that ASLA operates properly. We have evaluated the usefulness of ASLA in regard to time and accuracy. We have assessed the efficacy of the rule-based area correction method. As a result, we have verified that ASLA is useful.
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Onaga Haruki, Katayama Tetsuro, Kita Yoshihiro, Yamaba Hisaaki, Aburada Kentaro, Okazaki Naonobu
Journal of Robotics, Networking and Artificial Life 10 ( 4 ) 336 - 341 2024.9
Language：English Publishing type：Research paper (scientific journal) Publisher：ALife Robotics Corporation Ltd.
It is difficult for mobile application developers to understand the structure of large and complex mobile applications. To support iOS application development, we proposed SwiftDiagram: a visualization of the static structure of Swift source code, and demonstrated its usefulness. To further support them, this paper has implemented RAGESS(Real-time Automatic Generation of SwiftDiagram System), which is a software visualization tool. RAGESS performs static analysis on Swift source code and automatically generates the corresponding SwiftDiagram whenever the target project build succeeds.
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An Attempt to Solve Fill in the Missing Letters CAPTCHA Using Generative AI Reviewed
Yamaba H., Usuzaki S., Aburada K., Mukunoki M., Park M., Okazaki N.
Lecture Notes in Electrical Engineering 1322 LNEE 385 - 395 2024.8
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
This paper reports an attempt to solve the fill in the missing letters type CAPTCHA using generative AI. Many websites have adopted CAPTCHA to prevent bots and other automated programs from engaging in malicious activities such as posting comment spam. Text-based CAPTCHA is the most common and earliest form of CAPTCHA. However, as optical character recognition (OCR) technology has improved, the intensity of distortions applied to a CAPTCHA to keep it unrecognizable by OCR has also increased. This has reached a point where humans are having difficulty recognizing CAPTCHA text. The CAPTCHA proposed in the previous study asks users to spell a word by filling in some blanks. Since the number of letters displayed is minimal, it is challenging to identify the correct word. However, one or more images that can serve as hints to help users guess the answer word are also provided. It is expected that the ability to guess can distinguish between humans and computers. However, it is conceivable that generative AI, which has been advancing in recent years, can substitute for this ability. A series of experiments was carried out to evaluated the performance of the generative AI’s ability to solve the proposed CAPTCHA. First, we examined whether a well-known image recognition system could accurately identify the images used in the CAPTCHA problems. Next, we used the recognition results to have the generative AI solve the CAPTCHA problems and determined the accuracy rate. Additionally, we evaluated the performance of the generative AI itself by solving the problems using the correct identification of each image. From the experimental results, it was found that the CAPTCHA is relatively robust against attack techniques using generative AI.
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Evacuation Support System for Tsunami Disasters that Considers Shelter Congestion Reviewed
Nishi H., Nabeyama K., Usuzaki S., Aburada K., Yamaba H., Katayama T., Park M., Okazaki N.
Lecture Notes in Electrical Engineering 1322 LNEE 51 - 62 2024.8
Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
When a tsunami occurs, an evacuation support system capable of providing information on evacuation routes and shelter locations would enable evacuations to be conducted more swiftly and safely. In previous research, a proposal was made for an evacuation support system using low-power, long-distance communication. However, a challenge arose when shelter locations reached full capacity, requiring information to be shared, which in turn necessitated redirection of evacuees to alternative shelters. Therefore, in this study, a method for sharing shelter congestion information at an earlier stage was developed. The method of sharing based on the remaining capacity of shelters increased evacuation completion by up to 29 evacuees in an evacuee population of 1000.
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Usability improvement in color constancy CAPTCHA Reviewed
Usuzaki Shotaro, Yihan Wang, Aburada Kentaro, Yamaba Hisaaki, Takatsuka Kayoko, Katayama Tetsuro, Park Mirang, Okazaki Naonobu
IEICE Communications Express 13 ( 8 ) 331 - 334 2024.8
Language：English Publishing type：Research paper (scientific journal) Publisher：一般社団法人 電子情報通信学会
CAPTCHAs have traditionally been used to deter bots, but ensuring protection against machine learning attacks has become difficult. To tackle this problem, we proposed a CAPTCHA based on color constancy, a human cognitive ability that is difficult for machines to reproduce. Although this method achieved high attack resistance while maintaining usability compared to existing CAPTCHAs, the response time increased due to the color selection operation. To address this, we changed the task to a click-type format while keeping the original concept. Our experimental results show that our method matches the response times of existing CAPTCHAs without compromising attack resistance.
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Okazaki N., Usuzaki S., Waki T., Kawagoe H., Park M., Yamaba H., Aburada K.
Future Internet 16 ( 8 ) 2024.8
Publishing type：Research paper (scientific journal) Publisher：Future Internet
We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.
DOI： 10.3390/fi16080259
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Prototype of RAGESS Which Is a Tool for Automatically Generating SwiftDiagram to Support iOS App Development Reviewed
H. Onaga, T. Katayama, Y. Kita, H. Yamaba, K. Aburada, and N. Okazaki
Proc. 2024 Int'l Conf. on Artificial Life and Robotics (ICAROB 2024) 252 - 256 2024.2
Publishing type：Research paper (international conference proceedings)
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Extension to Support Types and Operation/Function Definitions in BWDM to Generate Test Case Tool from the VDM++ Specification Reviewed
S. Takakura, T. Katayama, Y. Kita, H. Yamaba, K. Aburada, and N. Okazaki
Proc. 2024 Int'l Conf. on Artificial Life and Robotics (ICAROB 2024) 257 - 261 2024.2
Publishing type：Research paper (international conference proceedings)
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Proposal of ASLA Which Is a Segmentation and Labeling Tool for Document Images Based on Deep Learning Reviewed
K. Kakinoki, T. Katayama, Y. Kita, H. Yamaba, K. Aburada, and N. Okazaki
Proc. 2024 Int'l Conf. on Artificial Life and Robotics (ICAROB 2024) 262 - 266 2024.2
Publishing type：Research paper (international conference proceedings)
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On an improvement of hand gesture recognition for realizing an s-EMG based user authentication using finger spelling Reviewed
Hisaaki Yamaba, Naoki Sawagashira, Kentaro Aburada, Tetsuro Katayama, Naonobu Okazaki
Proc. 2024 Int'l Symp. on Artificial Life and Robotics (ISAROB 2024) 933 - 938 2024.1
Authorship：Lead author, Corresponding author Publishing type：Research paper (international conference proceedings)
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Investigation of malware classification based on image representation Reviewed
Hyoga Kawagoe, Shotaro Usuzaki, Kentaro Aburada, Hisaaki Yamaba, Tetsuro Katayama, Mirang Park, Naonobu Okazaki
Proc. 2024 Int'l Symp. on Artificial Life and Robotics (ISAROB 2024) 929 - 932 2024.1
Publishing type：Research paper (international conference proceedings)
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Proposal of a DDoS Attack Detection Method Using the Communication Interval Reviewed
Iwasa K., Usuzaki S., Aburada K., Yamaba H., Katayama T., Park M., Okazaki N.
Lecture Notes in Electrical Engineering 1114 LNEE 165 - 174 2024
Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
As the scale of Distributed Denial of Service (DDoS) attacks has been escalating in recent years, the need for real-time detection of attacks has increased. Existing intrusion detection systems (IDSs) perform detection with a fixed window size (assumed to be in hours). In previous research, attack detection was performed by preparing windows of multiple sizes, selecting the appropriate window based on the state of the data, and using features learned in advance for that window size. Although this method yielded a high DDoS attack detection rate of 98.30%, it exhibited a considerable false-positive rate of 7.37%. The proposed method measures the communication intervals of identical packets within the window, identified as attack-related in the previous survey, and classifies those packets with an average communication interval below a set threshold as attacks. The experiment resulted in a 50.2% decrease in the false-positive rate.
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Proposal of Fill in the Missing Letters CAPTCHA Using Associations from Images Reviewed
Yamaba H., Mustaza M.N.F.B., Usuzaki S., Aburada K., Mukunoki M., Park M., Okazaki N.
Lecture Notes in Electrical Engineering 1114 LNEE 206 - 217 2024
Authorship：Lead author, Corresponding author Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
This paper proposes a new fill in the missing letters type CAPTCHA using associations from images. Many web sites have adopted CAPTCHA to prevent bots and other automated programs from malicious activities such as posting comment spam. Text-based CAPTCHA is the most common and earliest CAPTCHA. But as optical character recognition (OCR) technology has improved, the intensity of distortions that must be applied to a CAPTCHA for it to remain unrecognizable by OCR has increased. This has reached a point where humans are having difficulty recognizing CAPTCHA text. The idea of the proposed CAPTCHA asks users to spell a word by filling some blanks. Since the number of shown letters are few, it is difficult to answer the correct word. But one or more images that can be used as hints to guess what is the answer word are also shown to the users. A series of experiments was carried out to evaluated the performance of the proposed CAPTCHA. First, a computer program was developed with various software languages for the usability evaluation. The system was used for the experiments to find the suitable parameters of the CAPTCHA such as numbers of letters that will be disclosed, position of disclosed letters. Next, security evaluation experiments were carried out using the system under the obtained parameters. The results of the experiments showed that the performance and limitation of the proposed CAPTCHA.
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Study of an Image-Based CAPTCHA that is Resistant to Attacks Using Image Recognition Systems Reviewed
Nishikawa S., Usuzaki S., Aburada K., Yamaba H., Katayama T., Park M., Okazaki N.
Lecture Notes in Electrical Engineering 1114 LNEE 175 - 184 2024
Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
In today’s digital age, image-based CAPTCHAs are increasingly vulnerable to attacks using annotation services, which tag images and classify images according to their contents, or reverse image search services. To prevent such attacks, an image-based CAPTCHA was proposed that takes advantage of the fact that humans can correctly recognize images containing many discontinuous points, while existing image recognition systems misrecognize them. However, this CAPTCHA proved susceptible to attacks using noise reduction filters. The objective of the present study is to create a CAPTCHA using images that are resistant to such filters. Images used in the new CAPTCHA were realized by increasing the proportion of lines forming discontinuous surfaces in images. Experimental results demonstrated a human recognition rate of 95.8%, with the image recognition systems successfully identifying only one image overall. Moreover, when a noise reduction filter was applied, the recognition rate was lower than those reported in previous studies.