• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.662-675
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• 2018

Compression history detection plays an important role in digital multimedia forensics. Most existing works, however, mainly focus on digital image and video. Additionally, the existed audio compression detection algorithms aim to detect the trace of double compression. In real forgery scenario, multiple compression is more likely to happen. In this paper, we proposed a detection algorithm to reveal the compression history for MP3 audio. The statistics of the scale factor and Huffman table index which are the parameters of MP3 codec have been extracted as the detecting features. The experimental results have shown that the proposed method can effectively identify whether the testing audio has been previously treated with single/double/triple compression.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1134-1142
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• 2019

Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.58-60
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• 2019

Double JPEG compression detection is one of the most important ways of exposing the integrity of the JPEG image in image forensics. Several methods have been proposed for discriminating against the double JPEG image. In this paper, we propose a new method for restoring the JPEG compressed image and making the detector confused by introducing a Generative Adversarial Network (GAN). First, a generator network is designed for restoring the JPEG compressed image and analyzed the quality. Then, the restored image is tested with the double compression detector for evaluating the robustness of the proposed GAN model. The detection accuracy reduces from 98% to 58%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.43-52
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• 2021

IoT applications are quickly spread in many fields. Blockchain methods(BC), defined as a distributed sharing mechanism, offer excellent support for IoT evolution. The BC provides a secure way for communication between IoT devices. However, the IoT environments are threatened by hacker attacks and malicious intrusions. The IoT applications security are faced with three challenges: intrusions and attacks detection, secure communication, and compressed storage information. This paper proposed a system based on double-blockchain to improve the communication transactions' safety and enhance the information compression method for the stored data. Information security is enhanced by using an Ellipse Curve Cryptography(ECC) considered in a double-blockchain case. The data compression is ensured by the Compressed Sensing(CS) method. The conducted experimentation reveals that the proposed method is more accurate in security and storage performance than previous related works.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.119-126
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• 2021

The Internet of Things (IoT) applications are quickly spread in many fields. Blockchain methods (BC), defined as a distributed sharing mechanism, offer excellent support for IoT evolution. The BC provides a secure way for communication between IoT devices. However, the IoT environments are threatened by hacker attacks and malicious intrusions. The IoT applications security are faced with three challenges: intrusions and attacks detection, secure communication, and compressed storage information. This paper proposed a system based on double-blockchain to improve the communication transactions' safety and enhance the information compression method for the stored data. Information security is enhanced by using an Ellipse Curve Cryptography (ECC) considered in a double-blockchain case. The data compression is ensured by the Compressed Sensing (CS) method. The conducted experimentation reveals that the proposed method is more accurate in security and storage performance than previous related works.

• Journal of radiological science and technology
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• v.20 no.2
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• pp.63-67
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• 1997

This paper is to investigate a more detailed method for the diagnosis of anterior wall of the stomach by making a comparative study with several hospitals. It has been true that there have been hospitals, that have not examined anterior wall of the stomach. However, it is very important for us to examine anterior wall of the stomach for an carly detection of gastric carcinoma. The results of th study are as follows : 1. Frequency of occurrence of the early gastric carcinoma for the anterior wall were 50 cases and 34 cases for the posterior wall out of 84 cases. 2. Only a hospitals have examined the anterior wall of stomach. 3. In case of operation, only a hospitals have used two techniques at for same time single and double contrast studies. 4. Only cue hospital used a compression pad and three hospitals hod only filing state images taloen. 5. In general, 1 chest of film was used and the number of exposures rouged from 1 to 2 times. Lesions on the anterior wall of the stomach can be shown by the combination of prone single com-pression and supine double contrast radiographs. Therefore, the conclusion came to the result that the prone single compression and supine double contract technique of the anterior wall are Indispensable methods to the routine check of the stomach.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.