Back to main page Research Interests Teaching Publications Contact me Search the site

MSc

Project | Disciplines | Documents | Presentations and Animations


Project description

Institution: Institute of Computing at State University of Campinas

Theme: Analysis and implementation of hidden messages detection techniques in digital images.

Start: March 1st, 2004.   End: February 28th, 2006.

Thesis: Progressive Randomization for Steganalysis.

Defense date: February 17th, 2006.

Examining board:
  - Prof. Eduardo Antônio Barros da Silva (PhD. DEL/EE-PE/COPPE-UFRJ)
  - Prof. Ricardo Dahab (PhD. IC-Unicamp)
  - Prof. Julio Cesar López Hernández (PhD. IC-Unicamp).

Advisor: Prof. Siome Klein Goldenstein (PhD. at UPENN and Posdoc at Rutgers University).

Fellowship: FAPESP - Fundação de Amparo a Pesquisa do Estado de São Paulo.

Related publications: please access my publications area.

Up

Available documents

Master's thesis: Randomização Progressiva para Esteganálise. Thesis presented to and approved by an examining board on February 17th, 2006 at Instituto de Computação, Universidade Estadual de Campinas. Campinas, SP, Brasil.


Resumo

Neste trabalho, nós descrevemos uma nova metodologia para detectar a presença de conteúdo digital escondido nos bits menos significativos (LSBs) de imagens. Nós introduzimos a técnica de Randomização Progressiva (PR), que captura os artefatos estatísticos inseridos durante um processo de mascaramento com aleatoriedade espacial. Nossa metodologia consiste na progressiva aplicação de transformações de mascaramento nos LSBs de uma imagem. Ao receber uma imagem I como entrada, o método cria n imagens, que apenas se diferenciam da imagem original no canal LSB. Cada estágio da Randomização Progressiva representa possíveis processos de mascaramento com mensagens de tamanhos diferentes e crescente entropia no canal LSB. Analisando esses estágios, nosso arcabouço de detecção faz a inferéncia sobre a presença ou não de uma mensagem escondida na imagem I. Nós validamos nossa metodologia em um banco de dados com 20.000 imagens reais. Nosso método utiliza apenas descritores estatísticos dos LSBs e já apresenta melhor qualidade de classificação que os métodos comparáveis descritos na literatura.


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 I 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.

2.1 MB
1.3 MB

(PDF) (DJVU)


Up

Master's thesis proposal
Mastership proposal at State University of Campinas. It was presented to an examining board on October 22nd, 2004.

430.0 KB

(PDF)

Up

Presentations and Animations

Master's thesis presentation
Slides of my thesis presentation to the examining board on February 17th, 2006.

20 MB

(RAR)

Up

Master's thesis animations
Some animations of my thesis presentation to the examining board on February 17th, 2006.

Up

Master's thesis proposal presentation
Slides of my mastership proposal presentation to the examining board on October 22nd, 2004.

2.1 MB

(PDF)

Up