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Progressive randomization for steganalysis

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Author(s):
Anderson de Rezende Rocha
Total Authors: 1
Document type: Master's Dissertation
Press: Campinas, SP.
Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Computação
Defense date:
Examining board members:
Siome Klein Goldenstein; Eduardo Antonio Barros da Silva; Ricardo Dahab
Advisor: Siome Klein Goldenstein
Abstract

In this work, we describe a new methodology to detect the presence of hidden digital content in the Least Significant Bits (LSBs) of images. We introduce the Progressive Randomization technique that captures statistical artifacts inserted during the hiding process. Our technique is a progressive application of LSB modifying transformations that receives an image as input, and produces n images that only differ in the LSB from the initial image. Each step of the progressive randomization approach represents a possible content-hiding scenario with increasing size, and increasing LSB entropy. Analyzing these steps, our detection framework infers whether or not the input image I contains a hidden message. We validate our method with 20,000 real, non-synthetic images. Our method only uses statistical descriptors of LSB occurrences and already performs better than comparable techniques in the literature (AU)