English
A feature-based classification technique for blind image steganalysis
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578k
资源说明：Abstract—In contrast to steganography, steganalysis is focused
on detecting (the main goal of this research), tracking, extracting,
and modifying secret messages transmitted through a covert
channel. In this paper, a feature classification technique, based
on the analysis of two statistical properties in the spatial and
DCT domains, is proposed to blindly (i.e., without knowledge
of the steganographic schemes) to determine the existence of
hidden messages in an image. To be effective in class separation,
the nonlinear neural classifier was adopted. For evaluation, a
database composed of 2088 plain and stego images (generated by
using six different embedding schemes) was established. Based
on this database, extensive experiments were conducted to prove
the feasibility and diversity of our proposed system. It was found
that the proposed system consists of: 1) a 90%+ positive-detection
rate; 2) not limited to the detection of a particular steganographic
scheme; 3) capable of detecting stego images with an embedding
rate as low as 0.01 bpp; and 4) considering the test of plain images
incurred low-pass filtering, sharpening, and JPEG compression。
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