• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.194-199
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• 2022

Preprocessing for high-quality data is required for high accuracy and usability in various and complex image data-based industries. However, when a contaminated hostile example that combines noise with existing image or video data is introduced, which can pose a great risk to the company, it is necessary to restore the previous damage to ensure the company's reliability, security, and complete results. As a countermeasure for this, restoration was previously performed using Defense-GAN, but there were disadvantages such as long learning time and low quality of the restoration. In order to improve this, this paper proposes a method using adversarial examples created through FGSM according to image segmentation in addition to using the VQ-VAE model. First, the generated examples are classified as a general classifier. Next, the unsegmented data is put into the pre-trained VQ-VAE model, restored, and then classified with a classifier. Finally, the data divided into quadrants is put into the 4-split-VQ-VAE model, the reconstructed fragments are combined, and then put into the classifier. Finally, after comparing the restored results and accuracy, the performance is analyzed according to the order of combining the two models according to whether or not they are split.

• Journal of IKEEE
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• v.25 no.4
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• pp.766-769
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• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1197-1205
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• 2018

In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1132-1135
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• 2021

본 논문에서는 머신러닝 시스템에 심각한 오류를 발생시킬 수 있는 적대적 샘플을 제작하고, 이를 이용한 적대적 공격을 효과적으로 예방하고 방어할 수 있는 Adversarial Training 기반의 AI 백신을 개발하였으며, 본 논문이 제안하는 AI 백신이 적대적 샘플을 올바르게 인식하고 AI 공격 성공율을 현저하게 낮추는 등 강인성을 확보한 것을 실험을 통해 입증하였다. 아울러 스마트폰을 통해 수행결과를 확인할 수 있는 어플리케이션을 구현하여, 교육 및 시연 등을 통해 적대적 AI 공격에 대한 심각성을 인식하고 해당 방어과정을 명확히 이해할 수 있도록 하였다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.100-102
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• 2020

심층 신경망은 적대적인 공격으로 생성된 적대적 예제에 의해 쉽게 오작동할 수 있다. 이에 따라 다양한 방어 방법들이 제안되었으나, 더욱 강력한 적대적인 공격이 제안되어 방어 방법들을 무력화할 가능성은 존재한다. 이러한 가능성은 어떤 공격 범위 내의 적대적인 공격을 방어할 수 있다고 보장할 수 있는 인증된 방어(Certified defense) 방법의 필요성을 강조한다. 이에 본 논문은 인증된 방어 방법 중 가장 효과적인 방법의 하나로 알려진 구간 경계 전파(Interval Bound Propagation)의 성능을 향상하는 방법을 연구한다. 구체적으로, 우리는 기존의 구간 경계 전파 방법의 훈련 과정을 수정하는 방법을 제안하며, 이를 통해 기존 구간 경계 전파 방법의 훈련 시간을 유지하면서 성능을 향상할 수 있음을 보일 것이다. 우리가 제안한 방법으로 수행한 MNIST 데이터 셋에 대한 실험에서 우리는 기존 구간 경계 전파 방법 대비 인증 에러(Verified error)를 Large 모델에 대해서 1.77%, Small 모델에 대해서 0.96% 낮출 수 있었다.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.797-805
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• 2019

Threats targeting cyberspace are becoming more intelligent and increasing day by day. To cope with such cyber threats, it is essential to improve the coping ability of system security officers. In this paper, we propose a simulated threat generator that automatically generates cyber threats for cyber defense training. The proposed Simulated Threat Generator is designed with MITRE ATT & CK(Adversarial Tactics, Techniques and Common Knowledge) framework to easily add an evolving cyber threat and select the next threat based on the threat execution result.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1022-1024
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• 2022

최근 인공지능 기술이 급격하게 발전하고 빠르게 보급되면서, 머신러닝 시스템을 대상으로 한 다양한 공격들이 등장하기 시작하였다. 인공지능은 많은 강점이 있지만 인위적인 조작에 취약할 수 있기 때문에, 그만큼 이전에는 존재하지 않았던 새로운 위험을 내포하고 있다고 볼 수 있다. 본 논문에서는 데이터 유형 별 적대적 공격 샘플을 직접 제작하고 이에 대한 효과적인 방어법을 구현하였다. 영상 및 텍스트 데이터를 기반으로 한 적대적 샘플공격을 방어하기 위해 적대적 훈련기법을 적용하였고, 그 결과 공격에 대한 면역능력이 형성된 것을 확인하였다.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.145-156
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• 2023

Trajectory of ballistic missile is defined by inherent flight dynamics, which decided range and maneuvering characteristics. It is crucial to predict range and maneuvering characteristics of ballistic missile in KAMD (Korea Air and Missile Defense) to minimize damage due to ballistic missile attacks, Nowadays, needs for applying AI(Artificial Intelligence) technologies are increasing due to rapid developments of DNN(Deep Neural Networks) technologies. To apply these DNN technologies amount of data are required for superviesed learning, but trajectory data of ballistic missiles is limited because of security issues. Trajectory data could be considered as multivariate time series including many variables. And augmentation in time series data is a developing area of research. In this paper, we tried to augment trajectory data of ballistic missiles using recently developed methods. We used TimeVAE(Time Variational AutoEncoder) method and TimeGAN(Time Generative Adversarial Networks) to synthesize missile trajectory data. We also compare the results of two methods and analyse for future works.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.179-184
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• 2022

The accurate wing flexure model is required to improve the transfer alignment performance of guided weapon system mounted on a wing of fighter aircraft or armed helicopter. In order to solve this problem, mechanical or stochastical modeling methods have been studying, but modeling accuracy is too low to be applied to weapon systems. The deep learning techniques that have been studying recently are suitable for nonlinear. However, operating fighter aircraft for deep-learning modeling to secure a large amount of data is practically difficult. In this paper, it was used to generate amount of flexure data samples that are similar to the actual flexure data. And it was confirmed that generated data is similar to the actual data by utilizing "measures of similarity" which measures how much alike the two data objects are.