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Effective Robustness Improvement in Medical Image Segmentation : Adversarial Noise Removal by the Input Transform Method
http://doi.org/10.5626/JOK.2023.50.10.859
Adversarial attacks induce the model to make misjudgments by adding fine noise to the deep learning model input data. Deep learning in medical images raises the expectations for computer-assisted diagnosis, but there is a risk of being vulnerable to adversarial attacks. In addition, in the case of the double segmentation model, the defense of adversarial attacks is more difficult, but security studies related to this topic have not received attention. In this study, we perform FGSM attacks ony brain tumor segmentation models and employ input image transformation and gradient regularization as defenses against these attacks. The proposed application of JPEG compression and Gaussian filters effectively removes adversarial noise while maintaining performance in the original images. Moreover, the input image transformation method, when compared to the conventional gradient regularization model for achieving robustness, not only exhibits a higher defense performance but also offers the advantage of being applicable without the need for model retraining. Through this research, we identify vulnerabilities in the security of medical artificial intelligence and propose ensuring robustness that can be applied in the preprocessing stage of the model.
Application Monitoring System Design and Implementation using System Call Pattern
http://doi.org/10.5626/JOK.2022.49.10.795
A user application consists of a set of functions. An application gives a set of functions to do what the user needs. Applications that provide services such as web servers are very large and complex, making them a target for attackers. As a result of attacks by malicious hackers, application variables and program flow are distorted, leading to the hijacking of system administrator privileges or abnormal operations. In this paper, we designed and implemented a system that collects an application"s system call and detects anomalies in applications through the collected patterns. As a result of measuring the overhead through the actually implemented system, it was found that when about 1 million system calls were monitored, it had an overhead of about 0.8 seconds. This is about 1/28 of the overhead time of existing tools such as strace.
File-System-Level SSD Caching for Improving Application Launch Time
Changhee Han, Junhee Ryu, Dongeun Lee, Kyungtae Kang, Heonshik Shin
Application launch time is an important performance metric to user experience in desktop and laptop environment, which mostly depends on the performance of secondary storage. Application launch times can be reduced by utilizing solid-state drive (SSD) instead of hard disk drive (HDD). However, considering a cost-performance trade-off, utilizing SSDs as caches for slow HDDs is a practicable alternative in reducing the application launch times. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data that is essential in most other schemes. In particular, our scheme does not incur mapping overheads that cause significant burdens on the main memory, CPU, and SSD space for mapping table. Experimental results conducted with 8 popular applications demonstrate our scheme yields 56% of performance gain in application launch, when data blocks along with metadata are migrated.
Prefetching Framework for General Workloads Using Breakpoint
Kwangjin Ko, Junhee Ryu, Kyungtae Kang, Heonshik Shin
Application loading speed can be improved by timely prefetching disk blocks likely to be needed by an application. However, existing prefetchers if they are not specialized to a particular application incur high overheads and are poor at identifying the blocks that will actually be required. There are many sequences in which blocks may be needed and, even if two access sequences are identical, block tracing and access timings can be affected significantly by the state of the buffer cache. We propose a new application independent software based prefetching technique, in which breakpoints are inserted at appropriate places in an application to collect the information on correlations between the blocks and to prefetch the potential blocks ahead of their schedule based on it. Experiments on an HDD based desktop PC demonstrated an average 30% reduction in application launch time and 15% in general I/O, while reducing the wasted overhead.
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