• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.1
• /
• pp.12-21
• /
• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.5
• /
• pp.871-879
• /
• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.

• Defense and Technology
• /
• no.1 s.13
• /
• pp.13-18
• /
• 1980

• Defense and Technology
• /
• no.8 s.20
• /
• pp.6-13
• /
• 1980

• Defense and Technology
• /
• no.11 s.11
• /
• pp.6-15
• /
• 1979

• Defense and Technology
• /
• no.12 s.12
• /
• pp.7-19
• /
• 1979

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.449-458
• /
• 2023

Artificial Intelligence is providing convenience in various fields using big data and deep learning technologies. However, deep learning technology is highly vulnerable to adversarial examples, which can cause misclassification of classification models. This study proposes a method to detect and purification various adversarial attacks using StarGAN. The proposed method trains a StarGAN model with added Categorical Entropy loss using adversarial examples generated by various attack methods to enable the Discriminator to detect adversarial examples and the Generator to purification them. Experimental results using the CIFAR-10 dataset showed an average detection performance of approximately 68.77%, an average purification performance of approximately 72.20%, and an average defense performance of approximately 93.11% derived from restoration and detection performance.

• International journal of advanced smart convergence
• /
• v.8 no.1
• /
• pp.141-146
• /
• 2019

These days, there are many applications using neural networks as parts of their system. On the other hand, adversarial examples have become an important issue concerining the security of neural networks. A classifier in neural networks can be fooled and make it miss-classified by adversarial examples. There are many research to encounter adversarial examples by using denoising methods. Some of them using GAN (Generative Adversarial Network) in order to remove adversarial noise from input images. By producing an image from generator network that is close enough to the original clean image, the adversarial examples effects can be reduced. However, there is a chance when adversarial noise can survive the approximation process because it is not like a normal noise. In this chance, we propose a research that utilizes high-level representation in the classifier by combining GAN network with a trained U-Net network. This approach focuses on minimizing the loss function on high representation terms, in order to minimize the difference between the high representation level of the clean data and the approximated output of the noisy data in the training dataset. Furthermore, the generated output is checked whether it shows minimum error compared to true label or not. U-Net network is trained with true label to make sure the generated output gives minimum error in the end. At last, the remaining adversarial noise that still exist after low-level approximation can be removed with the U-Net, because of the minimization on high representation terms.

• Current Optics and Photonics
• /
• v.5 no.4
• /
• pp.391-401
• /
• 2021

In the present study, a hyperelastic constitutive model is built by complying with a simplified hyperelastic strain energy function, which yields the numerical solution for a deformed polydimethylsiloxane (PDMS) membrane in the case of axisymmetric hydraulic pressure. Moreover, a nonlinear equilibrium model is deduced to accurately express the deformation of the membrane, laying a basis for precise analysis of the optical transfer function. Comparison to experimental and simulated data suggests that the model is capable of accurately characterizing the deformation behavior of the membrane. Furthermore, the stretch ratio derived from the model applies to the geometrical optimization of the deformed membrane.

• Current Optics and Photonics
• /
• v.4 no.4
• /
• pp.277-285
• /
• 2020

Computer-generated holograms (CGH) are the core devices to solve the problem of freeform surface measurement. In view of the overlay error introduced in the manufacturing process of CGH, this paper proposes an evaluation model for analyzing the overlay error of CGH. The detection method of extracting CGH profile information by an ultra-depth of field micro-measurement system is presented. Furthermore, based on the detection method and technical scheme, the effect of overlay error on the wavefront accuracy of CGH can be evaluated.