Robust PDF Malware Detection with Image Visualization and Processing Techniques

2019 ◽  
Author(s):  
Andrew Corum ◽  
Donovan Jenkins ◽  
Jun Zheng
Keyword(s):  
Malware Detection ◽  
Image Visualization ◽  
Processing Techniques

scholarly journals Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

2021 ◽  
Author(s):  
Tajuddin Manhar Mohammed ◽  
Lakshmanan Nataraj ◽  
Satish Chikkagoudar ◽  
Shivkumar Chandrasekaran ◽  
B.S. Manjunath
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Malware Detection ◽  
Image Visualization

Malware detection in industrial internet of things based on hybrid image visualization and deep learning model

2020 ◽  
Vol 105 ◽  
pp. 102154 ◽  
Author(s):  
Hamad Naeem ◽  
Farhan Ullah ◽  
Muhammad Rashid Naeem ◽  
Shehzad Khalid ◽  
Danish Vasan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Malware Detection ◽  
Learning Model ◽  
Industrial Internet Of Things ◽  
Image Visualization ◽  
Industrial Internet ◽  
Deep Learning Model

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Digital phase analysis in interference electron microscopy

1988 ◽  
Vol 46 ◽  
pp. 842-843
Author(s):  
S. Hasegawa ◽  
T. Kawasaki ◽  
J. Endo ◽  
M. Futamoto ◽  
A. Tonomura
Keyword(s):  
Magnetic Field ◽  
Electron Microscopy ◽  
Phase Distribution ◽  
Magnetic Recording Media ◽  
Electron Wave ◽  
Vector Potentials ◽  
Weak Magnetic Fields ◽  
Leakage Magnetic Field ◽  
Optical Reconstruction ◽  
Processing Techniques

Interference electron microscopy enables us to record the phase distribution of an electron wave on a hologram. The distribution is visualized as a fringe pattern in a micrograph by optical reconstruction. The phase is affected by electromagnetic potentials; scalar and vector potentials. Therefore, the electric and magnetic field can be reduced from the recorded phase. This study analyzes a leakage magnetic field from CoCr perpendicular magnetic recording media. Since one contour fringe interval corresponds to a magnetic flux of Φo(=h/e=4x10-15Wb), we can quantitatively measure the field by counting the number of finges. Moreover, by using phase-difference amplification techniques, the sensitivity for magnetic field detection can be improved by a factor of 30, which allows the drawing of a Φo/30 fringe. This sensitivity, however, is insufficient for quantitative analysis of very weak magnetic fields such as high-density magnetic recordings. For this reason we have adopted “fringe scanning interferometry” using digital image processing techniques at the optical reconstruction stage. This method enables us to obtain subfringe information recorded in the interference pattern.

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