19-12-2012, 05:44 PM
Robust Image Alignment for Tampering Detection
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Abstract
The widespread use of classic and newest technologies
available on Internet (e.g., emails, social networks, digital repositories)
has induced a growing interest on systems able to protect the
visual content against malicious manipulations that could be performed
during their transmission. One of the main problems addressed
in this context is the authentication of the image received
in a communication. This task is usually performed by localizing
the regions of the image which have been tampered. To this aim the
aligned image should be first registered with the one at the sender
by exploiting the information provided by a specific component of
the forensic hash associated to the image. In this paper we propose
a robust alignmentmethod which makes use of an image hash
component based on the Bag of Features paradigm. The proposed
signature is attached to the image before transmission and then
analyzed at destination to recover the geometric transformations
which have been applied to the received image. The estimator is
based on a voting procedure in the parameter space of the model
used to recover the geometric transformation occurred into the
manipulated image. The proposed image hash encodes the spatial
distribution of the image features to deal with highly textured
and contrasted tampering patterns.
INTRODUCTION AND MOTIVATIONS
THE growing demand of techniques useful to protect
digital visual data against malicious manipulations is
induced by different episodes that make questionable the use
of visual content as evidence material [1], [2]. Specifically,
methods useful to establish the validity and authenticity of a
received image are needed in the context of Internet communications.
The problem of tampering detection can be addressed
using a watermarking-based approach. The watermark is inserted
into the image, and during tampering detection, it is
extracted to verify if there was a malicious manipulation on the
received image. A damage into the watermark indicates a tampering
of the image under consideration.
REGISTRATION COMPONENT
As previously stated, one of the common steps of tampering
detection systems is the alignment of the received image. Image
registration is crucial since all the other tasks (e.g., tampering
localization) usually assume that the received image is aligned
with the original one, and hence could fail if the registration is
not properly done. Classical registration approaches [11]–[13]
cannot be directly employed in the considered context due the
limited information that can be used (i.e., original image is not
available at destination and the image hash should be as short
as possible).
The schema of the proposed registration component is shown
in Fig. 1. As in [3], [4], and [7], we adopt a BOF-based representation
[15] to reduce the dimensionality of the descriptors to
be used as hash component for the alignment. Differently than
[4] and [7], we employ a transformation model and a voting
strategy to retrieve the geometric manipulation [16].
In the proposed system, a codebook is generated by clustering
the set of SIFT [17] extracted on training images. The clustering
procedure points out a centroid for each cluster. The set of centroids
represents the codebook to be used during the image hash
generation. The computed codebook is shared between sender
and receiver (Fig. 1). It should be noted that the codebook is
built only once, and then used for all the communications between
sender and receiver (i.e., no extra overhead for each communication).
TAMPERING LOCALIZATION COMPONENT
Once the alignment has been performed as described in
Section II, the image is analyzed to detect tampered regions.
Tampering localization is the process of localizing the regions
of the image that have been manipulated for malicious purposes
to change the semantic meaning of the visual message.
The tampering manipulation typically changes the properties
(e.g., edges distributions, colors, textures, etc.) of some image
regions. To deal with this problem the image is usually divided
into non-overlapping blocks which are represented through
feature vectors computed taking into account their content.
The feature vectors computed at the source are then sent to
a destination where these are used as forensic hash for the
tampering detection component of the system. The check to
localize tampered blocks is performed by the receiver taking
into account the received signature and the one computed
(with the same procedure employed by the sender) on the
received image. The comparison of the signatures is performed
block-wise after the alignment (see Section II).
CONCLUSION AND FUTURE WORKS
The main contribution of this paper is related to the alignment
of images in the context of distributed forensic systems. A
robust image registration component which exploits an image
signature based on the BOF paradigm has been introduced.
The proposed hash encodes the spatial distribution of features
to better deal with highly texturized and contrasted tampering
patches. Moreover, a non-uniform quantization of histograms
of oriented gradients is exploited to perform tampering localization.
The proposed framework has been experimentally
tested on a representative dataset of scenes. Comparative
tests show that the proposed approach outperforms recently
appeared techniques by obtaining a significant margin in
terms of registration accuracy, discriminative performances
and tampering detection. Future works should concern a more
in-depth analysis to establish the minimal number of SIFT
needed to guarantee an accurate estimation of the geometric
transformations and a study in terms of bits needed to represent
the overall image signature.