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Hiding in plain sight: handwriting and applications to steganography

Hahn, James (2019) Hiding in plain sight: handwriting and applications to steganography. Undergraduate Thesis, University of Pittsburgh. (Unpublished)

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Steganography, the process of hiding secret information within normal-looking data (a cover),
lies at the intersection of computer science and security. Recently, these covers have come in
the form of images. The steganographic task must pass two visual inspections: by humans
and by machines. If a human examines the image and fails to spot the hidden data, the
algorithm successfully passes the first test. The second task is preventing a machine from
detecting patterns to reverse engineer the secret information. In 2017, researchers achieved
some success [1], but there were two main issues: the steganography only worked with
(a) fully saturated, (b) fixed-size (100 x 100) images. To curb these limitations, a new
pipeline is explored to generate non-fixed size cover images with steganographic modification
rather than embedding. This paper explores this new form of steganography with several
key processes. First, the secret information is encrypted before combining it with the cover
using neural cryptography. Second, the information hides in the stroke data of a person's
handwriting on a white background, increasing task difficulty, forcing the steganographic
approach to be robust to a plethora of data, including sparse images. In this sense, the
strokes are directly modified, rather than inserted or embedded in-between pixels. The result
is a toy problem utilizing realistic, generated, coordinate sequences of human handwriting
modified with slight offsets dependent on the information combined with the coordinates in
the sequence. With these slight offsets, the new generated coordinates are nearly identical
to the original coordinates, preserving the primary structure of the handwriting, but shining
light on a new avenue of steganography based on data modification rather than embedding.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Hahn, Jamesjrh160@pitt.edujrh1600000-0003-1950-4914
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKovashka, Adrianakovashka@cs.pitt.edukovashka
Committee MemberHauskrecht, Milosmilos@cs.pitt.edumilos
Committee MemberLee, Adamadamlee@cs.pitt.eduadamlee
Committee MemberFragkiadaki,
Date: 23 April 2019
Date Type: Publication
Defense Date: 5 April 2019
Approval Date: 23 April 2019
Submission Date: 18 April 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 68
Institution: University of Pittsburgh
Schools and Programs: David C. Frederick Honors College
Dietrich School of Arts and Sciences > Computer Science
Degree: BPhil - Bachelor of Philosophy
Thesis Type: Undergraduate Thesis
Refereed: Yes
Uncontrolled Keywords: pittetd, theses, format, vision, steganography, encryption, security, machine learning, pattern recognition, applications
Date Deposited: 23 Apr 2019 20:13
Last Modified: 23 Apr 2019 20:13

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