• The KIPS Transactions:PartC
• /
• v.12C no.3 s.99
• /
• pp.379-386
• /
• 2005

Traditional watermarking technologies are symmetric method which embedding and detection keys are same. Although the symmetric watermarking method is easy to detect the watermark, has method has weakness against to malicious attacks to remove or modify the watermark information when the symmetric key is disclosure. Recently, the asymmetric watermarking method that has different keys to embed and detect is watched several researchers as a next generation watermarking technology. In this paper, we have expanded search space of secret key using the solution set of linear simultaneous equations. Secret key is generated by secure linear transformation method to prevent of guessing secret key from public key, and the correlation value between secret key and public key is high. At the results, the multi bits information can be embedded and high correlation value was detected after JPEG compression.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5592-5609
• /
• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.795-808
• /
• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.5112-5128
• /
• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• The Plant Pathology Journal
• /
• v.35 no.1
• /
• pp.71-76
• /
• 2019

$SdhB^{P225F}$ and $SdhB^{H272R}$ mutations have been found associated with boscalid resistance in Botrytis cinerea from strawberry in Shanghai, China. For rapid detection of two mutations, tetra-primers were designed and optimized to gain the relatively high accuracy and specificity based on the ARMS-PCR technique, by which resistance can be identified with different lengths of products on agarose gels. The tetra-primer ARMS-PCR systems for $SdhB^{P225F}$ and $SdhB^{H272R}$ were validated by 9 SdhB-squenced strains repeatedly. Then, sensitivity of 30 more strains were also tested by the methods, which were accordant with genotypes by sequencing and the sensitivity of conidial germination to boscalid by 100%. Thus, the methods developed in this study are proved to be rapid, inexpensive, accurate and practical for resistance detection of Botrytis cinerea caused by $SdhB^{P225F}$ and $SdhB^{H272R}$ mutations.

• Journal of Microbiology and Biotechnology
• /
• v.26 no.8
• /
• pp.1457-1463
• /
• 2016

Vibrio anguillarum, a devastating pathogen causing vibriosis among marine fish, is prevailing in worldwide fishery industries and accounts for grievous economic losses. Therefore, a rapid on-site detection and diagnostic technique for this pathogen is in urgent need. In this study, two mouse monoclonal antibodies (MAbs) against V. anguillarum, 6B3-C5 and 8G3-B5, were generated by using hybridoma technology and their isotypes were characterized. MAb 6B3-C5 was chosen as the detector antibody and conjugated with quantum dots. Based on MAb 6B3-C5 labeled with quantum dots, a modified dot blot assay was developed for the on-site determination of V. anguillarum. It was found that the method had no cross-reactivity with other than V. anguillarum bacteria. The detection limit (LOD) for V. anguillarum was 1 × 10³ CFU/ml in cultured bacterial suspension samples, which was a 100-fold higher sensitivity than the reported colloidal gold immunochromatographic test strip. When V. anguillarum was mixed with turbot tissue homogenates, the LOD was 1 × 10³ CFU/ml, suggesting that tissue homogenates did not influence the detection capabilities. Preenrichment with the tissue homogenates for 12 h could raise the LOD up to 1 × 10² CFU/ml, confirming the reliability of the method.

• Journal of the Korea Society for Simulation
• /
• v.19 no.1
• /
• pp.125-131
• /
• 2010

In wireless sensor networks application, sensor nodes are randomly deployed in wide and opened environment typically. Since sensor networks have these features, it is vulnerable to physical attacks in which an adversary can capture deployed nodes and use them to inject a fabricated report into the network. This threats of network security deplete the limited energy resource of the entire network using injected fabricated reports. A dynamic en-route filtering scheme is proposed to detect and drop the injected fabricated report. In this scheme, node executes the key redistribution to increases the detection power. It is very important to decide the authentication key redistribution because a frequent key redistribution can cause the much energy consumption of nodes. In this paper, we propose a key redistribution determining method to enhance the energy efficiency and maintain the detection power of network. Each node decides the authentication key redistribution using a fuzzy system in a definite period. The proposed method can provide early detection of fabricated reports, which results in energy-efficiency against the massive fabricated report injection attacks.

• Journal of the Optical Society of Korea
• /
• v.14 no.4
• /
• pp.444-450
• /
• 2010

The traditional approach to inverting aerosol extinction makes use of the assumption of a constant LIDAR ratio in the entire Mie-LIDAR signal profile using the Fernald method. For the large uncertainty in the cloud optical depth caused by the assumed constant LIDAR ratio, an not negligible error of the retrieved aerosol extinction below the cloud will be caused in the backward integration of the Fernald method. A new algorithm to determine aerosol extinction below a cirrus cloud from Mie-LIDAR signals, based on a new cloud boundary detection method and a Mie-LIDAR signal modification method, combined with the backward integration of the Fernald method is developed. The result shows that the cloud boundary detection method is reliable, and the aerosol extinction below the cirrus cloud found by inverting from the modified signal is more efficacious than the one from the measured signal including the cloud-layer. The error due to modification is less than 10% taken in our present example.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.41-60
• /
• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4240-4258
• /
• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.