• Title/Summary/Keyword: Detection Techniques

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A New Efficient Impulse Noise Detection based on Rank Estimation

  • Oh, Jin-Sung;Kim, You-Nam
    • Journal of the Institute of Convergence Signal Processing
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    • v.9 no.3
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    • pp.173-178
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    • 2008
  • In this paper, we present a new impulsive noise detection technique. To remove the impulse noise without detail loss, only corrupted pixels must be filtered. In order to identify the corrupted pixels, a new impulse detector based on rank and value estimations of the current pixel is proposed. Based on the rank and value estimations of the current pixel, the new proposed method provides excellent statistics for detecting an impulse noise while reducing the probability of detecting image details as impulses. The proposed detection is efficient and can be used with any noise removal filter. Simulation results show that the proposed method significantly outperforms many other well-known detection techniques in terms of image restoration and noise detection.

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BM3D and Deep Image Prior based Denoising for the Defense against Adversarial Attacks on Malware Detection Networks

  • Sandra, Kumi;Lee, Suk-Ho
    • International journal of advanced smart convergence
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    • v.10 no.3
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    • pp.163-171
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    • 2021
  • Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Block-matching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.

A Margin-based Face Liveness Detection with Behavioral Confirmation

  • Tolendiyev, Gabit;Lim, Hyotaek;Lee, Byung-Gook
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.187-194
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    • 2021
  • This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author's own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

Synthetic Image Generation for Military Vehicle Detection (군용물체탐지 연구를 위한 가상 이미지 데이터 생성)

  • Se-Yoon Oh;Hunmin Yang
    • Journal of the Korea Institute of Military Science and Technology
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    • v.26 no.5
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    • pp.392-399
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    • 2023
  • This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

Attack Detection on Images Based on DCT-Based Features

  • Nirin Thanirat;Sudsanguan Ngamsuriyaroj
    • Asia pacific journal of information systems
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    • v.31 no.3
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    • pp.335-357
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    • 2021
  • As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

A Multi-Sensor Fire Detection Method based on Trend Predictive BiLSTM Networks

  • Gyu-Li Kim;Seong-Jun Ro;Kwangjae Lee
    • Journal of Sensor Science and Technology
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    • v.33 no.5
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    • pp.248-254
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    • 2024
  • Artificial intelligence techniques have improved fire-detection methods; however, false alarms still occur. Conventional methods detect fires using current sensors, which can lead to detection errors due to temporary environmental changes or noise. Thus, fire-detection methods must include a trend analysis of past information. We propose a deep-learning-based fire detection method using multi-sensor data and Kendall's tau. The proposed system used a BiLSTM model to predict fires using pre-processed multi-sensor data and extracted trend information. Kendall's tau indicates the trend of a time-series data as a score; therefore, it is easy to obtain a target pattern. The experimental results showed that the proposed system with trend values recorded an accuracy of 99.93% for BiLSTM and GRU models in a 20-tap moving average filter and 40% fire threshold. Thus, the proposed trend approach is more accurate than that of conventional approaches.

Using Machine Learning Techniques for Accurate Attack Detection in Intrusion Detection Systems using Cyber Threat Intelligence Feeds

  • Ehtsham Irshad;Abdul Basit Siddiqui
    • International Journal of Computer Science & Network Security
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    • v.24 no.4
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    • pp.179-191
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    • 2024
  • With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

An Efficient Spectrum Sensing Technique for Wireless Energy Harvesting Systems (무선에너지하비스팅 시스템을 위한 효율적인 스펙트럼 센싱 기법)

  • Hwang, Yu Min;Shin, Yoan;Kim, Dong In;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.12 no.4
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    • pp.141-145
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    • 2017
  • Spectrum sensing is a critical functionality of Cognitive Radio(CR) systems and the CR systems can be applied to RF energy harvesting systems to improve an energy harvesting rate. There are number of spectrum sensing techniques. One of techniques is energy detection. Energy detection is the simplest detection method and is the most commonly used. But, energy detection has a hidden terminal problem in real wireless communication, because of secondary user (SU) can be affected by frequency fading and shadowing. Cooperative spectrum sensing can solve this problem using spatial diversity of SUs. But it has a problem of increasing data by processing multiple secondary. So, we propose the system model using adaptive spectrum sensing algorithm and system model is simulated. This algorithm chooses sensing method between single energy sensing and cooperative energy according to the received signal's Signal to Noise Ratio (SNR) from Primary User (PU). The simulation result shows that adaptive spectrum sensing has an efficiency and improvement in CR systems.

Fast Face Detection in Video Using The HCr and Adaptive Thresholding Method (HCr과 적응적 임계화에 의한 고속 얼굴 검출)

  • 신승주;최석림
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.61-71
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    • 2004
  • Recently, various techniques for face detection are studied, but most of them still have problems on processing in real-time. Therefore, in this paper, we propose novel techniques for real-time detection of human faces in sequential images using motion and chroma information. First, background model is used to find a moving area. In this procmoving area. edure, intensity values for reference images are averaged, then skin-color are detected in We use HCr color-space model and adaptive threshold method for detection. Second, binary image labeling is applied to acquire candidate regions for faces. Candidates for mouth and eyes on a face are obtained using differences between green(G) and blue(B), intensity(I) and chroma-red(Cr) value. We also considered distances between eye points and mouth on a face. Experimental results show effectiveness of real-time detection for human faces in sequential images.

PE Header Characteristics Analysis Technique for Malware Detection (악성프로그램 탐지를 위한 PE헤더 특성 분석 기술)

  • Choi, Yang-Seo;Kim, Ik-Kyun;Oh, Jin-Tae;Ryu, Jae-Cheol
    • Convergence Security Journal
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    • v.8 no.2
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    • pp.63-70
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    • 2008
  • In order not to make the malwares be easily analyzed, the hackers apply various anti-reversing and obfuscation techniques to the malwares. However, as the more anti-revering techniques are applied to the malwares the more abnormal characteristics in the PE file's header which are not shown in the normal PE file, could be observed. In this letter, a new malware detection technique is proposed based on this observation. For the malware detection, we define the Characteristics Vector(CV) which can represent the characteristics of a PE file's header. In the learning phase, we calculate the average CV(ACV) of malwares(ACVM) and normal files(ACVN). To detect the malwares we calculate the 2 Weighted Euclidean Distances(WEDs) from a file's CV to ACVs and they are used to decide whether the file is a malware or not. The proposed technique is very fast and detection rate is fairly high, so it could be applied to the network based attack detection and prevention devices. Moreover, this technique is could be used to detect the unknown malwares because it does not utilize a signature but the malware's characteristics.

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