This document discusses literature on lossless visible watermarking and lossless image recovery. It begins by introducing digital watermarking and classifying methods as visible or invisible. Reversible watermarking allows removal of embedded watermarks and restoration of the original content. The document then reviews existing watermarking techniques in the spatial, frequency and wavelet domains. It proposes a novel method for generic visible watermarking using deterministic one-to-one compound mappings that are reversible, allowing lossless recovery of original images from watermarked images. This approach can embed various visible watermarks of arbitrary sizes into images in a lossless manner.

1. LITERATURE REVIEW ON GENERIC LOSSLESS VISIBLE WATERMARKING &
LOSSLESS IMAGE RECOVERY
1
D. Phaneendra, 2
I.Suneetha, 3
A. Rajani,
M.Tech(DECS) Student, Associate professor & Head, Assistant professor
Department of ECE, AITS
Annamacharya Institute of Technology and Sciences,Tirupati,India-517520
1
dorasalaphanendrakumarreddy@gmail.com
2
iralasuneetha.aits@gmail.com
3
rajanirevanth446@gmail.com
Abstract — One way for copyright protection is
digital watermarking. Digital watermarking is the
process of embedding information regarding the
authenticity or the identity of the owners into a image
or any piece of data. Digital watermarking has been
classified into two types: Visible and Invisible
Watermarking. By the use of Reversible watermarking
the embedded watermark can be removed and restore
the original content. The lossless image recovery is a
difficult task but; it is important in most of the
applications where the quality of the image is
concerned. There are many methods for visible
watermarking with lossless image recovery. One to One
compound mapping is one of the technique. The
compound mapping is reversible and it allows lossless
recovery of original images from the watermarked
images. Security protection measures can be used to
prevent illegal attackers.
Key Terms: Reversible visible watermarking,
Discrete Cosine Transform (DCT), Discrete Fourier
Transform (DFT), Discrete Wavelet Transform (DWT).
I. INTRODUCTION
The concepts of authenticity and copyright
protection are of major importance in the framework
of our information society. For example, TV channels
usually place a small visible logo on the image corner
(or a wider translucent logo) for copyright protection.
In this way, unauthorized duplication is discouraged
and the recipients can easily identify the video
source. Official scripts are stamped or typed on
watermarked papers for authenticity proof. Bank
notes also use watermarks for the same purpose,
which are very difficult to reproduce by conventional
photocopying techniques.
Digital Image watermarking methods are usually
classified into two types: visible and invisible [1-7].
The invisible watermarking aims to embed copyright
information into host media, in case of copyright
infringements, to identify the ownership of the
protected host the hidden information can be
retrieved. It is important that the watermarked image
must be resistant to common image operations which
ensure that the hidden information after alterations is
still retrievable without any defect that means the
recovered image is same as the original. On the other
hand, methods of the visible watermarking yield
visible watermarks. These visible watermarks are
generally clearly visible after applying common
image operations. In addition, ownership information
is conveyed directly on the media and copyright
violations attempts can be deterred.
In general Embedding of watermarks, degrade the
quality of the host media. The legitimate users are
allowed to remove the embedded watermark and
original content can be restored as needed using a
group of techniques, namely reversible watermarking
[8-11]. However, lossless image recovery is not
guaranteed by all reversible watermarking
techniques, which means that the recovered image is
same as the original. Lossless recovery is important
where there is serious concerns about image quality
such as include forensics, military applications,
historical art imaging, or medical image analysis.
The most common approach is to embed a
monochrome watermark using deterministic and
reversible mappings of pixel values or DCT
coefficients in the watermark region [6,9,11].
Another is to rotate consecutive watermark pixels to
embed watermark that is visible [11].the watermarks
of arbitrary sizes can be embedded into any host
image. Only binary visible watermarks can be
embedded using these approaches.
The lossless visible watermarking is proposed by
using one-to-one compound mappings which allow
mapped values to be controllable The approach is
generic, leading to the possibility of embedding
different types of visible watermarks into cover
images. Two applications of the proposed method are
demonstrated; where we can embed opaque
monochrome watermarks and non-uniformly
translucent full-color ones into color images.
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2. II. RELATED WORK
2.1 EXISTING WATERMARKING
TECHNOLOGIES
A. Spatial-Domain technologies
Spatial-domain technologies refer to those
embedding watermarks by directly changing pixel
values of host images. Some common spatial domain
algorithms include Least Significant Bit (LSB). The
LSB is the most straight-forward method of
watermark embedding. The most serious drawback of
spatial-domain technologies is limited robustness.
In the spatial domain, pixels in randomly selected
regions of the image are modified according to the
signature or logo desired by the author of the product.
This method involves modifying the pixel values of
the original image where the watermark should be
embedded. Fig. 1 shows the block diagram of a
spatial-domain data embedding system.
Fig.1. Spatial domain data embedding system
Randomly selected image data are dithered by a
small amount according to a predefined algorithm,
whose complexity may vary in practical systems. The
algorithm defines the intensity and the position of the
watermark on the original image. One of the major
disadvantages of the conventional watermarking is
that it can be easily extracted from the original image
which makes this technique unsuitable for copyright
authentication. There are three factors that determine
the parameters of the algorithm applied in the spatial
domain watermarking. The three factors are:
• The information associated with the signature.
Basically, the signature is the watermark
embedded on the original image. The
information of the signature is closely related to
the size and quality of the signature.
• The secret random key.
The secret key may be included in the process of
watermarking to improve the security during
transmission. If a key is also included, only the
receiver who knows the key can extract the
watermark, and not any intruders.
• The masking property of the image.
The masking property of the image is also related
to the quality and composition of the image which
signifies the clarity of the watermark on the
original image.
One form of the data embedding algorithm is given
by the equation ,
ŷ=y +αI
Where y(i,j), is the original image intensity at pixe
position (i,j), ŷ is the watermarked image, and αI
represents the embedded data in the form of small
changes in intensity levels. The author of the
watermark holds two keys:
• The region of the image where the logo is
marked and
• The information in the watermark, αI.
Given the marked image, the original owner will be
able to recover the watermark by comparing the
marked image with the original. In the reconstruction
of the embedded watermark, the following
computation is made,
I= (ŷ-y)/α
Fig. 2. Watermarking result of a color host image. (a) Host
image. (b) Logo image. (c) Resulting watermarked Image.
(d) Watermark extracted image.
Fig.2. shows the output results of spatial domain
technique with host image, logo image, watermarked
image and watermark extracted image respectively.
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3. It is difficult for spatial-domain watermarks to
survive under attacks such as lossy compression and
low-pass filtering. Also the information can be
embedded in spatial domain is very limited
B. Frequency-Domain Technologies
Compared to spatial-domain watermark,
watermark in frequency domain is more robust and
compatible to popular image compression standards.
Thus frequency-domain watermarking obtains much
more attention. To embed a watermark, a frequency
transformation is applied to the host data. Then,
modifications are made to the transform coefficients.
Possible frequency image transformations include the
Discrete Fourier Transform (DFT), Discrete Cosine
Transform (DCT) and others
The first efficient watermarking scheme was
introduced by Koch et al. In their method, the image
is first divided into square blocks of size 8x8 for
DCT computation. A pair of mid-frequency
coefficients is chosen for modification from 12
predetermined pairs. Bors and Pitas developed a
method that modifies DCT coefficients satisfying a
block site selection constraint. After dividing the
image into blocks of size 8x8, certain blocks are
selected based on a Gaussian network classifier
decision. The middle range frequency DCT
coefficients are then modified, using either a linear
DCT constraint or a circular DCT detection region. A
DCT domain watermarking technique based on the
frequency masking of DCT blocks was introduced by
Swanson. Cox developed the first frequency-domain
watermarking scheme. After that a lot of
watermarking algorithms in frequency domain have
been proposed.
Figure 3 and Figure 4 illustrate the watermark
embedding and detection/extraction in frequency
domain, respectively. Most frequency-domain
algorithms make use of the spread spectrum
communication technique. By using a bandwidth
larger than required to transmit the signal, we can
keep the SNR at each frequency band small enough,
even the total power transmitted is very large. When
information on several bands is lost, the transmitted
signal can still being recovered by the rest ones. The
spread spectrum watermarking schemes are the use of
spread spectrum communication in digital
watermarking. Similar to that in communication,
spread spectrum watermarking schemes embed
watermarks in the whole host image. The watermark
is distributed among the whole frequency band. To
destroy the watermark, one has to add noise with
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4. sufficiently large amplitude, which will heavily
degrade the quality of watermarked image and be
considered as an unsuccessful attack.
One major reason why frequency domain
watermarking schemes are attractive is their
compatibility with existing image compression
standards, in particular, the JPEG standard. The
compatibility ensures those schemes a good
performance when the watermarked image is subject
to lossy compression, which is one of the most
common image processing methods today.
Besides its own advantages it has a disadvantage
that it is not suitable for visible watermarking. And
only invisible watermarking is mostly performed in
frequency domain.
C. Wavelet-domain Technologies
The wavelet transform is identical to a hierarchical
sub-band system, where the sub-bands are
logarithmically spaced in frequency. The basic idea
of the DWT for a two dimensional image is described
as follows. An image is first decomposed into four
parts of high, middle and low frequencies (i.e. LL1,
HL1, LH1, HH1 sub bands) by critically sub-
sampling horizontal and vertical channels using
Daubechies filters. The sub-band HL1, LH1 and HH1
represent the finest scale of wavelet coefficients as
shown in figure 5. To obtain the next coarser scaled
wavelet coefficient, the sub-band LL1 is further
decomposed and critically sub-sampled. This process
is repeated several times, which is determined by the
application in hand. An example of an image
decomposed into ten sub-bands for three levels is
shown in Figure 6. Each level has various bands
information such as low-low, low-high, high-low and
high-high frequency bands.
Fig.5. Three level wavelet decomposition
Fig.6. (a) Three-level Decomposition. (b) Coefficient
Distribution.
Furthermore, from these DWT coefficients, the
original image can be reconstructed. For
reconstruction process same filter must be used. This
reconstruction process is called the inverse DWT
(IDWT). If I (m, n) represent an image, the DWT and
IDWT for I (m, n) can be similarly defined by
implementing the DWT and IDWT on each
dimension m and n separately.
III. CONCLUSION
In this paper we have briefly discussed regarding
the methods (Spatial domain, Frequency domain and
Wavelet domain) which are formerly used in visible
watermarking. The former methods used DCT, DFT,
DWT and LSB (Least Significant Bit) for desired
visible watermarking.
A novel method for generic visible watermarking
with a capability of lossless image recovery is
proposed. The method is based on the use of
deterministic one-to-one compound mappings of
image pixel values for overlaying a variety of visible
watermarks of arbitrary sizes on cover images. The
compound map-pings are proved to be reversible,
which allows for lossless recovery of original images
from watermarked images. The mappings may be
adjusted to yield pixel values close to those of desired
visible watermarks. Different types of visible
watermarks, including opaque monochrome and
translucent full color ones, are embedded as
applications of the proposed generic approach. A
two-fold monotonically increasing compound
mapping is created and proved to yield more
distinctive visible watermarks in the watermarked
image. Security protection measures by parameter
and mapping randomizations have also been
proposed to deter attackers from illicit image
recoveries. Experimental results demonstrating the
effectiveness of the proposed approach are also
included.
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5. REFERENCES
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