International Journal of Engineering Trends and Technology- May to June Issue 2011
Watermarking Systems Engineering: Enabling Digital
Assets Security and Other Applications
S.Vimala1, J.Saravanan2 ,T.S. Sumi1
1
Faculty, M.C.A, Panimalar Engineering College, Chennai, 2 P.G Scholar, M.C.A, Panimalar Engineering College, Chennai,
3
Faculty, M.C.A, Panimalar Engineering College, Chennai.
Abstract- Watermarking embeds ownership information in
digital content. Watermark describes information that can be
used to prove the ownership of relational database. Here the
embedding is hidden that the presence of watermarking is
invisible to the user. The proposed approach permits us to
successfully combine the secure embedding of client side with the
superior robustness of informed embedding techniques,
providing a new powerful tool for the secure distribution of highquality multimedia contents. Open issues in the proposed
framework to be addressed in the future research concern the
need of higher security and the compression overhead. Clientside watermark embedding systems have been proposed as a
possible solution for the copyright protection in large-scale
content distribution environments. In this framework, we
propose a new look-up-table-based secure client-side embedding
scheme properly designed for the spread transform dither
modulation watermarking method.
Index Terms- content distribution, secure watermark
embedding, multiple decryption, ST-DM informed embedding,
fingerprinting.
I. INTRODUCTION
Content protection measure for the prevention of copyright
violations from unauthorized user is the new challenge in large
scale content distribution environments. Watermarking is
solution for this problem. This technique is used to embed into
each copy the content a unique code identifying a particular
user or device.
Current content distribution systems are based on a clientserver architecture, where the watermark embedding is usually
carried out by a trusted server. However in large scale
distribution systems the server may become overloaded, due to
the fact that the computational burden grows linearly with the
number of users. In addition, since the distribution of
individually watermarked copies requires point-to- point
connections, bandwidth requirements can become prohibitive.
A possible solution consists in using client-side watermark
embedding: the server is allowed to send a unique copy of the
content to all the interested users through broadcasting. Each
client will be in charge of embedding a watermark identifying
the received copy. Since the client is untrusted, the users
should not have access to the original content or to the secret
information required to embed the watermark. In secure
watermark embedding schemes, the server transmits the same
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encrypted version of the original content to all the clients but a
client-specific decryption key allows decrypting the content
and at the same time to implicitly embed a watermark to
obtain a uniquely watermarked version of the content.
Here we are interested in techniques based on encryption
systems that allow the use of multiple decryption keys, which
decrypt the same cipher text to slightly different plaintexts.
The difference between original and decrypted content
represents thus the hidden watermark. A special stream cipher,
called Chameleon, which allows decrypting the cipher text in
slightly different ways. During encryption, a sequence of
indices is used to select four entries from a look-up-table
(LUT) for each plaintext element. These entries are XORed
with the plaintext to form the cipher text. The decryption is
identical to encryption except for the use of a decryption LUT
that superimposes some errors onto the content, thus leaving a
unique watermark. Generalizations of Chameleon, suitable for
embedding spread spectrum watermarks, have also been
proposed.
In this paper, we propose a LUT based secure embedding
system designed for the Spread Transform Dither Modulation
(STDM) algorithm belonging to the class of data hiding
schemes defined informed embedding algorithms or hostinterference rejecting methods.
II. LUT BASED WATERMARK EMBEDDING
The LUT based secure embedding proposed in works as
follows. The distribution server generates a long- term master
encryption look-up table E of size T, whose entries E[t] are
independently and randomly generated according to a
Gaussian distribution. The LUT E will be used to encrypt the
content to be distributed to the KU clients. Next, for the k-th
client, the server generates a personalized watermark LUT Wk
according to a desired probability distribution, and builds a
personalized decryption LUT Dk by combining componentwise master encryption LUT E and watermark LUT Wk:
Dk[t] = −E[t] +Wk[t], t= 0, 1, . . . , T – 1
(1)
The personalized LUTs are then transmitted once to each
client over a secure channel.
A content, represented as a vector x of size M, is encrypted
by adding to each element R entries of the LUT E pseudo
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International Journal of Engineering Trends and Technology- May to June Issue 2011
randomly selected according to a session key sk. The
encrypted content c is sent to all the authorized clients along
with the session key sk. The k-th client can decrypt c by using
his/her personalized decryption LUT Dk, with the final effect
that a spread-spectrum watermark sequence is embedded into
the decrypted content yk, through an additive rule. In detail,
driven by the session key sk, a set of M・R values tij in the
range [0, T −1] is generated, where 0 ≤ i ≤ M−1, 0 ≤ j ≤ R−1.
Each feature xi is encrypted by adding R entries of E,
obtaining the encrypted feature ci:
Joint decryption and watermarking is accomplished by
reconstructing with sk the same sequence of indices tij and by
adding R entries of Dk to each encrypted feature ci:
Starting from an original vector composed by M features, a
M×Md projection matrix S = (s1, s2,. . . , sMd ) whose
columns are orthogonal is generated. The host features are
projected according to S, which, differently from the
traditional ST-DM, needs to be known to the clients; in order
to add a level of secrecy, only L out of Md projections will be
quantized to embed the watermark, where the L directions are
kept secret to the clients. Let us indicate by A the indexes
corresponding to the L directions where the watermark will be
introduced. To represent that only L out of Md projections are
quantized we will resort to a M × L matrix SA denoting a
partition of S obtained by picking the columns whose indexes
are in A.
To embed the watermark, it is chosen to use not just two
quantizers, but a set of L dithered quantizers, shifted each by a
factor θj with respect to a reference quantizer Q0() having a
fixed step size Δ, so that, for j ∈ A
Qj(x) = Q0(x) + θj .
(5)
Each dithered quantizer is used to quantize one of the L
randomly chosen projections, so the marked components are
where j ∈ A.
The vector of watermarked features is then given by:
The result of this operation is the sequence of watermarked
content features yk identifying the k-th user.
A. Quantization Index Modulation approach and Spread
Transform Dither Modulation
The ST-DM algorithm belongs to the wider class of
Quantization Index Modulation (QIM) watermarking [5].
According to the QIM approach, watermark embedding is
achieved through the quantization of the host feature vector x
on the basis of a set of predefined quantizers, where the
particular quantizer depends on the to-be-hidden message. In
the case of ST-DM, the correlation between the host feature
vector x and a reference spreading signal s is computed as
In a forensic application, we can think that each k-th user
can be identified by employing a different set of dithered
quantizers, characterized by a dithering vector θk = {θk , j}j∈A.
According to this approach, referring to equation (6), in yk it
is possible to distinguish between a term present in all the
watermarked copies of the content, the summation
and a term identifying the single k-th user, i.e. the summation
This correlation is then quantized by applying to it either a
quantizer Q0, or a quantizer Q1, depending on the to-behidden bit, obtaining the quantized correlation rw. The
watermarked features are then:
y = x + (rw − rx)s
(4)
To recover the embedded bit, a minimum distance decoder
applied to the correlation r’ of the watermarked and possibly
attacked features ˜y with the vector s is adopted. The ST-DM
approach can be extended in such a way that the host features
are projected not only along one direction, but on a vector
subspace, allowing to introduce an additional degree of
freedom in the design of the scheme.
III. ST-DM SECURE EMBEDDING
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The detector will thus try to identify a dishonest client by
looking at this uniquely distinguishing component.
A. ST-DM client side embedding
Let us now describe how we implement a ST-DM based
secure client side embedding. A distribution server, like in [4]
generates an encryption look-up table E, whose entries are
i.i.d. random variables following a Gaussian distribution N(0,
σE); moreover, for each client, a personalized watermark LUT
Wk is generated, according to N(0, σW), and a decryption LUT
Dk is computed by combining component-wise E and Wk. In
addition, let us suppose that the projection matrix S has been
generated. The personalized LUTs and the matrix S are then
transmitted once to each client over a secure channel.
The server encrypts a content x of size M by adding to it
some entries of E; however, differently from Eq. (2), here R
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International Journal of Engineering Trends and Technology- May to June Issue 2011
entries are added along each of the Md orthogonal directions sj.
In addition, in L randomly chosen directions the common
terms
approach based on a correlation statistic, followed by a
maximum detector. Namely, the detector computes a vector
of quantization errors as
present in the embedding rule are introduced, so that at the
server side the host features will be modified as in the
following:
and the detector statistics for the k-th client is defined as:
The decision is made according to the following test:
Decryption and watermark embedding is driven by the
session key sk needed to reconstruct the sequence of indices tjh
and add Md・R entries of the decryption LUT Dk to each
encrypted feature ci:
,then:
The output of the test is either the index k of the guilty
client or the special symbol k = ∅ meaning that no watermark
has been found on the examined content.
The threshold γ has to be set so as to minimize the
probability of detection errors. To do so, we formulate the
problem as a binary hypothesis testing where the hypotheses
If we assume it is possible to set
are: H0, the content is not watermarked; Hk∗, the content
,
the result of this operation is the sequence of watermarked
features yk, using the dithered quantizers shifted by the set θk =
{θk,j}j∈A identifying the k-th user. The joint decryption and
watermarking process then becomes:
The final effect of the joint decryption and watermarking, is
that a ST-DM watermark has been embedded in L directions,
but in (Md −L) directions a spread spectrum like noise has
been added. These noise terms can not be avoided, since the
client is not allowed to know the L out of Md directions,
indicated by the set A.
IV. DETECTION
It is assumed that the input to the detector is a vector of
possibly altered watermarked features, denoted as ˜y. Such a
vector is projected onto the L directions carrying the
watermark, yielding a vector of L watermarked projections:
Since the embedding rule makes it difficult to define a
likelihood ratio, the proposed detector relies on a suboptimal
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contains the watermark of the k ∗-th client. The detector makes
an error every time it accuses a client and no watermark was
present (false alarm) or it fails in detecting the watermark of
the k∗-th client because it decides that no watermark is present
(missed detection) or it wrongly accuses an innocent client
(wrong accusation). The performance of the detector is then
measured by the probability of false alarm Pf , the probability
of missed detection Pm, and the probability of wrong
accusation Pw:
Hence, the probability of correct detection should be
expressed as Pd = 1 − Pm − Pw. The above error probabilities
will depend on the threshold γ and on several other parameters
of the system, and will allow to measure the performance of
the proposed system.
V. PERFORMANCE EVALUATION
The system embeds a watermark into a gray level image by
modifying m out of 64 block DCT coefficients. In particular,
for each 8×8 block, the DCT coefficients are reordered in the
zig-zag scan, and the ones from the second until the (m+1)th
are selected. Since the host features have variable size,
whereas the spreading vector has a fixed size (i.e. M), we
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divide the vector of available host features into chunks of
length M: each chunk is composed by the DCT coefficients
belonging to l blocks, so that the length of each chunk is
M = l ∙ m. If the number of 8 × 8 blocks inside an image is
NB, the number of available chunks NC is given by:
NC = (m · NB) /M.
For each chunk the same projection matrix is adopted, but
different sets of dithering θj are considered. According to this,
the k-th client is identified by a vector obtained as the
concatenation of NC vectors θik of length L (one for each
chunk), that is in detection the vector
having size L・ NC = L(m·NB)/M will be used.
The performance analysis is carried out by defining the
operating conditions in terms of Document to Watermark
Ratio (DWR). The DWR expresses the ratio between the
power of the host features and that of the watermark:
where (MD/M)(m/64) and (L/M)(m/64)represent the
percentages of DCT coefficients suffering the quantization
error introduced by the embedding process, due to the shift
addition process (involving all the MD directions) and to the
quantization process (involving only L directions)
respectively.
In order to force a given DWR value for a specific
watermarked image, we introduce a parameter α controlling
the watermark strength and we put it as a factor multiplying
the watermark LUT; specifically, we will consider αE[tjh] and
αDk[tjh] instead of E[tjh] and Dk[tjh] in equations (7) and (8),
and consequently also the watermark LUT will result
multiplied by α. Since this parameter is required in decryption,
the server will need to send the adopted α value to all the
clients.
The relationship between α and the DWR can be computed
by taking into account that to assure that the shift value θ
remains inside the interval [−Δ/2,+Δ/2], and given that θ
follows a Gaussian distribution, the standard deviation σθ of
the shifts θ has to be chosen in such a way that σθ = Δ/8 .
Furthermore, since a shift value is obtained by the addition of
R entries of the LUT Wk, we have that σθ2= Rα2σW2.
By considering that Δ2 = 64σθ2, equation (18) can thus be
rewritten as:
In order to compare the performances of the proposed STDM client-side watermarking system vs. the SS version, we
implemented the two systems considering the following values
for the system parameters: M = 32, L = 4, m = 4, MD = 32, R
= 4, σE = 100, σW = 0.01, T = 216, DWR = 36 dB. Parameters
α, Δ and σθ have then been derived from the fixed ones, while
σx and NB are estimated from the testing image (512 × 512
8-bit grey level images were considered). The Pf has been set
to 10 −3.
ST-DM shows a vanishing probability of missed detection
at high WNR/JPEG quality and performs better than SS when
the degradation on the watermarked content is kept within an
acceptable range.
VI. CONCLUSIONS
The proposed client-side ST-DM embedding has been
compared with a previously proposed client-side SS
watermarking system, showing that the advantages of the
informed embedding technique, which are well known in
traditional watermarking systems, are preserved in the clientside scenario. This is not an obvious result, since the clientside framework imposes some constraints that do not allow us
to embed a pure ST-DM watermark.
Client side watermark embedding schemes have been
proposed as a possible solution for the copyright protection in
large scale content distribution environments. In this
framework, we propose a lookup- table based secure
embedding system, designed for the Spread Transform Dither
Modulation (ST-DM) watermarking algorithm, that
outperforms Spread Spectrum based systems.
From the point of view of the compression overhead, in the
current system, the server distributes encrypted images that
cannot be optimally compressed; to cope with it, two possible
solutions are devised: in the first one, suitable for the
distribution of very large contents like videos, the server
selectively encrypts only some parts of the content, which are
not compressed; in the second one, the server employs some
form of distributed source coding, that, at least in theory,
should guarantee a coding rate close to the entropy of the
watermarked content. However, it has to be considered that
already in the current system the overhead due to encryption is
balanced by the possibility of broadcasting the encrypted
content: if the number of clients is much greater than the best
achievable compression rate, transmitting separate optimally
compressed copies of the content to each client will consume
more bandwidth than broadcasting a single but less
compressed copy.
and therefore by imposing a given watermark distortion (i.e. a
given DWR), a proper value for α is achieved.
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