Collaborators

Graduates: Diman Zad Tootaghaj Danial M.M. Kamani Yu Luo Undergrads: Sahil Mishra Luke Porupski Jeremy Ong Yizhi Huang Jeffery Cao Shengguang Bai

Publications

Discovering Triangles in Portraits for Supporting Photographic Creation, S He, Z Zhou, F Farhat, JZ Wang, IEEE Transactions on Multimedia, Aug 2017. Intelligent Portrait Composition Assistance — Integrating Deep-learned Models and Photography Idea Retrieval, F Farhat, MM Kamani, S Mishra, JZ Wang, July 2017. Skeleton Matching with Applications in Severe Weather Detection, MM Kamani, F […]

Leveraging big visual data to predict severe weather

NEWS SOURCEs [www.sciencedaily.com/releases/2017/06/170621145133.htm www.eurekalert.org/pub_releases/2017-06/ps-nir062117.php phys.org/news/2017-06-leverages-big-severe-weather.html sciencenewsline.com/news/2017062217300053.html] Every year, severe weather endangers millions of people and causes billions of dollars in damage worldwide. But new research from Penn State’s College of Information Sciences and Technology (IST) and AccuWeather has found a way to better predict some of these threats by harnessing the power of big data. […]

Shape matching for automated bow echo detection

Abstract: Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive thunderstorm conditions such as damaging winds, hail and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high […]

Thumbnail Generation Engine by Smart Cropping

Given any aspect ratio, oval shape, or automatically AutoThumbGen generates a thumbnail based on the input image. In fact, the most prominent part of the input image is recognized and captured by the app with a proper thumbnail size. The source code is in C. The code has been also embedded in Android via JNI and PHP by […]

Blind detection of low-rate embedding

Abstract: Steganalysis of least significant bit (LSB) embedded images in spatial domain has been investigated extensively over the past decade and most well-known LSB steganography methods have been shown to be detectable. However, according to the latest findings in the area, two major issues of very low-rate (VLR) embedding and content-adaptive steganography have remained hard […]

Image Steganalysis of Low Rate Embedding in Spatial Domain

Abstract LSB embedding in spatial domain with very low rate can be easily performed and its detection in spite of many researches is very hard, while BOSS competition has been held to break an adaptive embedding algorithm with low rate. Thus, proposing powerful steganalyzer of very low rate in spatial domain is highly requested. In […]

Eigenvalues-based LSB steganalysis

Abstract: So far, various components of image characteristics have been used for steganalysis, including the histogram characteristic function, adjacent colors distribution, and sample pair analysis. However, some certain steganography methods have been proposed that can thwart some analysis approaches through managing the embedding patterns. In this regard, the present paper is intended to introduce a […]

SVD and Noise Estimation based Image Steganalysis

Abstract: We propose a novel image steganalysis method, based on singular value decomposition and noise estimation, for the spatial domain LSB embedding families. We first define a content independence parameter, DS, that is calculated for each LSB embedding rate. Next, we estimate the DS curve and use noise estimation to improve the curve approximation accuracy. […]

Risk of attack coefficient effect on availability of adhoc networks

Abstract: Security techniques have been designed to obtain certain objectives. One of the most important objectives all security mechanisms try to achieve is the availability, which insures that network services are available to various entities in the network when required. But there has not been any certain parameter to measure this objective in network. In […]