• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.602-609
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• 2002

In this paper, we propose an robust invisible watermarking method using wavelet transform and singular value decomposition for the ownership protection. of images. For this method, after we decompose the original image in three level using wavelet transform, we use singular value decomposition based key depended watermark insertion method in the lowest band $LL_3.$ And we also watermark using DCT for extraction of watermark and verification of robustness. In the experiments, we found that it had a good quality and robustness in attack such as compression, image processing, geometric transformation and noises. Especially, we know that this method have very high extraction ratio against nose and JPEG compression. And Digimarc's method can not extract watermark in 80 percent compression ratio of JPEG, but the proposed method can extract well.

• Spatial Information Research
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• v.21 no.3
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• pp.31-43
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• 2013

Although the vector map data possesses much higher values than other types of multimedia, the data copyright and the protection against illegal duplication are still far away from the attention. This paper proposes a novel watermarking technique which is both robust to diverse attacks and optimized to a vector map structure. Six approaches are proposed for the design of the watermarking algorithm: point-based approach, building a minimum perimeter triangle, watermark embedding in the length ratio, referencing to the pixel position of the watermark image, grouping, and using the one-way function. Our method preserves the characteristics of watermarking such as embedding effectiveness, fidelity, and false positive rate, while maintaining robustness to all types of attack except a noise attack. Furthermore, our method is a blind scheme in which robustness is independent of the map data. Finally, our method provides a solution to the challenging issue of degraded robustness under severe simplification attacks.

• Annual Conference of KIPS
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• 2003.05a
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• pp.655-658
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• 2003

멀티미디어 기술과 인터넷의 급속한 발전으로 인해 영상 데이터의 복사가 더욱 쉬워지고 있다. 이러한 작품들을 보호하기 위해 데이터 안에 저작권을 표시할 수 있는 기술들이 필요해지고 지난 몇 년간 데이터에 다른 정보를 삽입할 수 있는 많은 기법들이 제안되어 왔다. 본 논문에서는 RGB 칼라 영상을 인간 시각 특성을 나타내는 I.UV 좌표계로 변화하여 인간 시각에 둔감한 U영역에 키를 이용하여 랜덤하게 워터마크를 삽입하고 추출하는 워터마킹 기법을 제안한다. 또한 웨이브릿(Wavelet) 변환을 사용하였으며 비교적 공격에 강한 고주파수 영역에 삽입하였고 추출시는 삽입한 키 값을 이용하여 워터마크를 추출한다. 제안한 워터마킹 기법은 시각적으로 보이지 않고, 가우시안 노이즈(Gaussian Noise) 및 필터링(filtering)에도 견고함을 보인다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.243-244
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• 2024

IoT 장치의 개수가 급증함에 따라 네트워크 환경에서 송수신되는 데이터 양이 증가하였고, 이에 따라 데이터 전송과정의 보안 강화가 중요해지고 있다. 기존에는 데이터에 인공 노이즈를 추가하는 차등 프라이버시 기법(Differential Privacy, DP)을 적용하여 데이터를 보호하고 있다. 하지만 DP가 적용된 데이터를 수신하는 정상 사용자의 머신러닝 학습 정확도가 감소되는 문제가 있다. 본 논문에서는 고신뢰 데이터 전송을 위한 데이터 인코딩 기반의 DP 기법인 EN-DP (Encoding-based DP) 모델을 제안한다. 실험 결과에 따르면, EN-DP 를 통한 정상 사용자와 공격자 간의 학습 능력 정확도 간극을 종래 모델 대비 최대 17.16% 개선할 수 있음을 입증하였다.

• Journal of KIISE
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• v.45 no.3
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• pp.288-293
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• 2018

There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker's background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.481-491
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• 2024

Digital watermarking is a technique for embedding information into digital content. Digital watermarking has attracted attention as a technique to combat piracy and identify artificially generated content, but it is still not robust in various situations. In this paper, we propose a frequency conversion-based hierarchical watermarking technique capable of attack detection, error correction, and owner identification. By embedding attack detection and error correction signatures in hierarchical watermarking, the proposed scheme maintains invisibility and outperforms the existing methods in capacity and robustness. We also proposed a framework to evaluate the performance of the image quality and error correction according to the type of error correction signature and the number of signature embeddings. We compared the visual quality and error correction performance of the conventional model without error correction signature and the conventional model with hamming and BCH signatures. We compared the quality by the number of signature embeddings and found that the quality deteriorates as the number of embeddings increases but is robust to attacks. By analyzing the quality and error correction ability by error correction signature type, we found that hamming codes showed better error correction performance than BCH codes and 41.31% better signature restoration performance than conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.175-183
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• 2011

Performance of digital video watermarking is an assessment that hides a lot of information in digital videos. Therefore, it is required to find a way that enables to store lots of bits of data into a high quality video of the frequency area of digital contents. Hence, this paper designs a watermarking system improving security with an enhancing watermarking based on invisible watermarking and embedding an watermarking on LH and HL subband and its subband by transforming wavelet after the extraction of luminance component from the frames of video by compromising robustness and invisible of watermarking elements. The performance analysis of security of watermarking is carried out with a statistic method, and makes an assessment of robustness against variety of attacks to invisible watermarking. We can verify the security of watermarking against variety of attacks by testing robustness and invisible through carrying out general signal processing like noise addition, lossy compression, and Low-Pass filtering.