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 […]

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 […]

EECS PSU

Farshid Farhat @ EECS PSU PhD Candidate School of Electrical Engineering and Computer Science The Pennsylvania State University Address: 310 IST Building, University Park, PA, 16802. Email: fuf111 AT psu DOT edu Web: Farshid Farhat ‘s Site@PSU About Me I am a member of Intelligent Information Systems (IIS) research lab at Penn State. I am working with Prof. […]

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. […]

Multi-dimensional correlation steganalysis

Abstract: Multi-dimensional spatial analysis of image pixels have not been much investigated for the steganalysis of the LSB Steganographic methods. Pixel distribution based steganalysis methods could be thwarted by intelligently compensating statistical characteristics of image pixels, as reported in several papers. Simple LSB replacement methods have been improved by introducing smarter LSB embedding approaches, e.g. […]