Articles via Databases
Articles via Journals
Online Catalog
E-books
Research & Information Literacy
Interlibrary loan
Theses & Dissertations
Collections
Policies
Services
About / Contact Us
Administration
Littman Architecture Library
This site will be removed in January 2019, please change your bookmarks.
This page will redirect to https://digitalcommons.njit.edu/dissertations/28 in 5 seconds

The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Forensic research on detecting seam carving in digital images
Author: Ye, Jingyu
View Online: njit-etd2017-055
(xv, 93 pages ~ 2.9 MB pdf)
Department: Department of Electrical and Computer Engineering
Degree: Doctor of Philosophy
Program: Electrical Engineering
Document Type: Dissertation
Advisory Committee: Shi, Yun Q. (Committee chair)
Shih, Frank Y. (Committee member)
Carpinelli, John D. (Committee member)
Hou, Edwin (Committee member)
Liu, Xuan (Committee member)
Date: 2017-05
Keywords: Digital image forensics
Seam carving detection
Support vector machine
Deep learning
Convolution neural network
Availability: Unrestricted
Abstract:

Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’.

To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious.

Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected.


If you have any questions please contact the ETD Team, libetd@njit.edu.

 
ETD Information
Digital Commons @ NJIT
Theses and DIssertations
ETD Policies & Procedures
ETD FAQ's
ETD home

Request a Scan
NDLTD

NJIT's ETD project was given an ACRL/NJ Technology Innovation Honorable Mention Award in spring 2003