• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.2
• /
• pp.662-675
• /
• 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 the Institute of Convergence Signal Processing
• /
• v.2 no.1
• /
• pp.12-18
• /
• 2001

SAR radar and FLIR images, which are taken from sensors on aircrafts or satellites, are compressed prior to transmission to facilitate rapid transfer through the limited bandwidth channels. In this case, it is important that it achieves compression ratio as high as possible as well as high target detection rate. In this paper a joint ATR-compression system based on the subband coding and VQ is proposed, which utilizes the encoder as a predictor or classifier for target detection. Simulation result shows that the proposed system achieves a relatively high level of target detection performance as well as a high compression ratio over 200:1.

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

• Transactions of the Korean Society of Automotive Engineers
• /
• v.16 no.4
• /
• pp.151-158
• /
• 2008

Gasoline Homogeneous Charge Compression Ignition (HCCI) combustion is a new combustion concept. Unlike the conventional internal combustion engine, the premixed fuel mixture with high residual gas rate is auto-ignited and burned without flame propagation. There are several operating factors which affect HCCI combustion such as start of combustion (SOC), residual gas fraction, engine rpm, etc. Among these factors SOC is a critical factor in the combustion because it affects exhaust gas emissions, engine power, fuel economy and combustion characteristics. Therefore SOC of gasoline HCCI should be controlled precisely, and SOC detection should be preceded SOC control. This paper presents a control oriented SOC detection method using 50 percent normalized difference pressure. Normalized difference pressure is defined as the normalized value of difference pressure and difference pressure is difference between the in-cylinder firing pressure and the motoring pressure. These methods were verified through the HCCI combustion experiments. The SOC detection method using difference pressure provides a fast and precise SOC detection.

• Journal of Broadcast Engineering
• /
• v.26 no.6
• /
• pp.738-747
• /
• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• Journal of Sensor Science and Technology
• /
• v.32 no.6
• /
• pp.481-486
• /
• 2023

Anomaly detection holds paramount significance across diverse fields, encompassing fraud detection, risk mitigation, and sensor evaluation tests. Its pertinence extends notably to the military, particularly within the Warrior Platform, a comprehensive combat equipment system with wearable sensors. Hence, we propose a data-compression-based anomaly detection approach tailored to unlabeled time series and sequence data. This method entailed the construction of two distinctive features, typicality and atypicality, to discern anomalies effectively. The typicality of a test sequence was determined by evaluating the compression efficacy achieved through the pattern dictionary. This dictionary was established based on the frequency of all patterns identified in a training sequence generated for each sensor within Warrior Platform. The resulting typicality served as an anomaly score, facilitating the identification of anomalous data using a predetermined threshold. To improve the performance of the pattern dictionary method, we leveraged atypicality to discern sequences that could undergo compression independently without relying on the pattern dictionary. Consequently, our refined approach integrated both typicality and atypicality, augmenting the effectiveness of the pattern dictionary method. Our proposed method exhibited heightened capability in detecting a spectrum of unpredictable anomalies, fortifying the stability of wearable sensors prevalent in military equipment, including the Army TIGER 4.0 system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.11a
• /
• pp.58-60
• /
• 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 Multimedia Society
• /
• v.8 no.1
• /
• pp.19-26
• /
• 2005

The basis function of wavelet transform used in this paper is constructed by using lifting scheme, which is different from general wavelet transform. Lifting scheme is a new biorthogonal wavelet con-structing method, that does not use Fourier transform for constructing its basis function. In this paper, an image compression and reconstruction method using the lifting scheme was proposed. And this method improves data visualization by supporting a partial reconstruction and a local reconstruction. Approx- imations at various resolutions allow extracting various sizes of feature from an image or signal with a small amount of original information. An approximation with small size of scaling coefficients gives a brief outline of features at fast. Image compression and edge detection techniques provide good frame- works for data management and visualization in multimedia database.

• Smart Structures and Systems
• /
• v.32 no.3
• /
• pp.179-193
• /
• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.11
• /
• pp.1356-1363
• /
• 2016

In this paper, we propose a novel error detection algorithm for LZ78-compressed data. The conventional error detection method adds a certain number of parity bits in transmission, and the receiver checks the number of bits representing '1' to detect the errors. These conventional methods use additional bits resulting in increased redundancy in the compressed data which results in reduced effectiveness of the final compressed data. In this paper, we propose error detection algorithm using the structural properties of LZ78 compression without using additional bits in the compressed data. The simulation results show that the error detection ratio of the proposed algorithm is about 1.3 times better for error detection than conventional algorithms.