Security Issues in SIFT and Video Halftoning
Date Issued
2012
Date
2012
Author(s)
Hsu, Chao-Yung
Abstract
With the advances in mobile device and cloud computing technologies, cloud computing
provider focuses on providing various multimedia applications as services to mobile
device users. The new type of multimedia application is changing people’s life. On the
other hand, it also lead to several multimedia security issues. In this dissertation, the
multimedia security issues will be described.
For a mobile device, video halftoning is a key technology for use in electronic paper
(e-paper) or smart paper, which is an emerging display device that has received considerable
attention recently. In this dissertation, a temporal frequency of flickering-distortion
optimized video halftoning method is proposed. We first uncover three visual defects that
conventional neighboring frame referencing-based video halftoning methods, due to their
sequential changes of reference frames, will encounter. To deal with the problem, we then
propose a reference frame update per GOP-based error diffusion video halftoning method
based on a flickering sensitivity-based human visual model. To efficiently compromise
between average temporal frequency of flickering (ATFoF) and visual quality, temporal
frequency of flickering-distortion (TFoFD) is presented as a metric for video halftoning
performance evaluation. Based on the proposed probability model of video halftoning,
the TFoFD curve can be accurately estimated to optimize the tradeoff between quality
and ATFoF before the video is halftoned. Our temporal frequency of flickering-distortion
optimization strategy can also be applied to other video halftoning schemes for performance
improvement.
With the advances in mobile device and cloud computing technologies, people are
getting used to accessing and querying multimedia data in the cloud environment. Scale space image feature extraction (SSIFE) has been widely adopted in multimedia security
and other applications for cloud service. However, the security threat to SSIFE-based media
security applications, which will be addressed in this thesis, is relatively unexplored.
The security threat, composed of a constrained-optimization keypoint inhibition attack
(KIHA) and a keypoint insertion attack (KISA), is specifically designed in the proposed
method for scale-space feature extraction methods such as SIFT and SURF. The principle
of KIHA is to make a fool of feature extraction protocols in that the detection rules are
purposely violated so that no local maximum can be found around in a local region. On
the other hand, KISA is designed to create the false positive problem. Our method is evaluated
and compared with Do et al.’s method (ACM MM’10), which also figures out the
weakness of our previous work (ACM MM’09). In addition, our proposed security threat
is applied to an image copy detection method operated on a web-scale image database for
performance evaluation.
In addition, privacy has received considerable attention but is still largely ignored
in the multimedia community. Consider a cloud computing scenario where the server is
resource-abundant and is capable of finishing the designated tasks. It is envisioned that
secure media applications with privacy preservation will be seriously treated. In view of
the fact that scale-invariant feature transform (SIFT) has been widely adopted in various
fields, this dissertation is the first to target the importance of privacy-preserving SIFT (PPSIFT)
and to address the problem of secure SIFT feature extraction and representation in
the encrypted domain. As all of the operations in SIFT must be moved to the encrypted
domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic
encryption. In our method, homomorphic comparison is a key component
for PPSIFT feature detection, but it is still a challenging issue for homomorphic encryption methods, like the Paillier cryptosystem. To solve this problem, the idea here is to
investigate a homomorphic comparison strategy via quantization. We also analyze the
error probability of feature extraction due to a scaling factor being introduced to realize
an integer DoG transform in the Paillier cryptosystem. Moreover, we show through the
security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure
against ciphertext only attack and known plaintext attack. Experimental results obtained
from different case studies demonstrate that the proposed homomorphic encryption-based
privacy-preserving SIFT performs comparably to original SIFT and that our method is
useful in SIFT-based privacy-preserving applications.
Subjects
Halftoning
Video
Security
SIFT
Feature Extraction
Encryption
Type
thesis
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