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Survey of robust and imperceptible watermarking
Namita Agarwal
1
&Amit Kumar Singh
2
&Pradeep Kumar Singh
1
Received: 25 October 2018 /Revised: 20 December 2018 /Accepted: 26 December 2018 /
Published online: 8 January 2019
#Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Robustness, imperceptibility and embedding capacity are the preliminary requirements of any
watermarking technique. However, research concluded that these requirements are difficult to
achieve at same time. In this paper, we review various recent robust and imperceptible
watermarking methods in spatial and transform domain. Further, the paper introduces elemen-
tary concepts of digital watermarking, characteristics and novel applications of watermark in
detail. Furthermore, various analysis and comparison of different notable watermarking
techniques are discussed in tabular format. We believe that our survey contribution will helpful
for fledgling researchers to develop robust and imperceptible watermarking algorithms for
various practical applications.
Keywords Watermarking .Imperceptible .Robustness .Security .Capacity
1 Introduction
There has been a great increase in data transmission across various networks and channels for past
few decades. Nowadays, with the development of technologies, the usage and transmission of
computerized media are growing. To establish the authenticity and avoid the misuse, data should
be secured by methods like watermarking which can be done for multimedia data. Digital
watermarking averts illegal and malevolent copying and dissemination of digital images by
hiding the unremarkable ownership data in the host image [29,79]. Watermarking is a process
of embedding a single/dual watermark in terms of a tag, label or digital signal into a cover media.
Multimedia Tools and Applications (2019) 78:8603–8633
https://doi.org/10.1007/s11042-018-7128-5
*Amit Kumar Singh
amit_245singh@yahoo.com
Namita Agarwal
namita312@gmail.com
Pradeep Kumar Singh
pradeep_84cs@yahoo.com
1
Department of CSE & IT, JUIT, Solan, HP, India
2
Department of CSE, NIT Patna, Patna, India
In [4], watermark process can be defined on the basis of domain and different groups [4].
According to domain based, we can divide watermarking techniques either in spatial or in
transform domain [65]. The spatial domain approaches are initially used techniques, where
watermark embedding can be done by changing the image pixels directly. It has advantages of
low computational cost and accessible to implement. The most common in this domain is the least
significant bit (LSB) and spread spectrum and correlation based. However, discrete cosine
transforms (DCT), discrete wavelet transforms (DWT), discrete Fourier transform (DFT), singular
value decomposition (SVD) and Karhunen-Loeve transform (KLT) are the potential example of
transform domain. In the context of visibility, visible and invisible are the two different categories
of the digital watermark. Further, robust and fragile are the different classes of invisible watermark
[4]. The detail classification of watermark is presented in [4,99,118].
1.1 Watermark embedding and extraction process
Figures 1and 2shows the general process of watermark embedding and extraction [100,106],
respectively. The original cover and watermark data, and a secret key generate the
watermarked data in the watermark embedding process (Fig. 1). However, the watermark
recovery technique is the function of watermarked image/ original data, key and test data.
Same key is used for the processes. (Fig. 2).
1.2 Types of watermark systems
There are three different classes of watermarking systems reliant upon the description and
consolidation of contributions and productions [67,100,106,119]. The summary of these
systems are discussed below:
Decoder
Watermark
Secret Key
Encoder
Watermark
Secret Key
a) Embedding Process
b) Extraction Process
Test Data
Extracted
Data
Original Data
Watermarked
Data
Fig. 1 Wat er mark aEmbedding and bExtraction Process
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1.) Blind Watermarking: In this type of watermarking system, extraction of watermark needs
only a watermarked image, it does not require an original image. The notable applications
of blind watermarking are healthcare, copyright protection, electronic voting system etc.
2.) Non-blind Watermarking: In such system, it copies the original image and the embedded
watermark that are necessary along with the test data for extraction. Potential applications
of this type of watermarking system are covert communication and copyright protection.
3.) Semi-blind Watermarking: It works as non-blind system, deprived of requiring the
original data for detection. Some of the important applications of such system are image
authentication, CAD models etc.
1.3 Characteristics of watermark
There are numerous vital characteristics that watermark illustrates, which are very imperative for
digital watermarking systems [106]. Figure 3depicts the elementary characteristics of watermarking.
Fig. 2 Important types watermarking system
Fig. 3 Characteristics of watermark
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The capability of an algorithm to repel to noise is defined as robustness. Security
means watermark is difficult to alter or remove without destroying the cover image. The
data payload is expressed as the amount of information that it contains. Imperceptibility
leads to the transparency of watermark. Fragile watermark focused on authentication
purpose [106]. Key restrictions are taken into consideration as another different charac-
teristic that it is the level of restriction employed on the capability to read a watermark.
Computational cost is described as the total cost convoluted in enclosing and revealing
the watermarks. Rest of the important characteristics are well defined in [106].
1.4 Applications of watermark
Potential researchers are using different watermarking schemes for numerous evolving appli-
cations. The applications of watermark include copyright protection, digital forensic, military,
digital forensic, healthcare, medical applications and so on. Some of the applications are
presentedinFig.4[50,121].
1.) Copyright protection: The main goal of this watermark application is providing copy-
right protection to digital information by hiding secret information.
2.) Broadcast monitoring: It is an application that allows content proprietors to automati-
cally verify when, where and for how much time a content was broadcast through cable,
satellite television and worldly.
3.) Fingerprinting: It is a process by which the watermarked content contains the intended
recipient’s identification information in order to trace back the source of illegal
distribution.
4.) Medical applications: The watermarking techniques offer authentication and confiden-
tiality of medical data in a reversible manner. Research established that watermarking
approaches are providing a value added security tools for healthcare applications.
5.) Electronic Voting System: Internet has reached too many villages, country as well as
worldwide due to quick evolution in computer network. Electronic voting system is a
process of accompanying elections by preserving the security during the time of
Fig. 4 Applications of watermark
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elections. Due to the rapid use of internet in every field like banking, shopping,
submission of tax returns, secure transaction is necessary. Clearly, it is an alternate
solution for the conduction of elections by considering the security maintenance in the
process of election. The most valuable solution for all of these problems can be achieved
by implementing digital watermarking.
6.) Remote Education: due to unavailability of teachers and other problems in villages,
distance education is becoming more powerful technique to provide education. For
distance learning a resilient requirement of smart technology is needed to develop a
remote education. Watermarking is used here to provide authentication to the transmis-
sion of data in distance learning.
7.) Chip and Hardware Protection: Mohanty et al. [71] introduced role of watermarking in
hardware design protection. Intellectual property (IP) core and hardware protection is a
multi-layered problem containing Trojan Security, buyer ownership security, security
against IP Piracy and vendor ownership [71]. On the basis of designer’s choice, digital
watermarking can be embedded in multi-level of hardware design concept.
8.) Secure data on Cloud: With the increase of images in day to day life, content-based
image retrieval is considered. Images take more storage as compared to text. So, for the
maintenance of images, cloud storage is an example [130]. Some sensitive images such
as medical and non-medical images need to be authenticated before transferring to
another place. By the cloud server a unique watermark is inserted in to the encrypted
images before images are sent to user. When an illegal image is found, by the watermark
extraction method an unauthorised user can be outlined.
In addition, we have identified some significant applications of watermarking according to
characteristics of digital watermark in brief [106]. (Table 1).
Table 1 Identified applications of watermarking according to their characteristics
Characteristics Definitions Applications
Robustness Robustness is described as an
ability of an algorithm to
resist against attacks [120].
Copyright protection
Imperceptibility It means, when perceived quality
of original image should not
be damaged by the existence
of watermark [49].
Digital imaging, telemedicine,
digital documents
False Positive Rate It is defined as a watermark
in a particular Work and
does not exists in real.
Copy control, ownership
Fragility It defends the embedded
watermark against
malevolent attacks [50].
Authentication of data and
integrity verification of
multimedia data
Security Security is the capability to resist
against intentional attacks.
Telemedicine, digital imaging,
telecommunication, multimedia
Capacity It defines by the number of watermarks
embedding in a data at a similar time.
Media distribution, Auxiliary data
embedding, tele-medicine
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1.5 Watermarking attacks
In many watermarking applications, the noticeable data is probable to be treated in some way
before it spreads the watermark receiver [96]. In terms of watermarking, attack is defined by
any processing that may harm uncovering of the hidden secret information or communication
of the information carried through watermark. Further, attacked data is described by processed
watermarked data. Categorizations of some attacks are described below [106].
a.) Active attacks: In this attack, hacker efforts intentionally to remove the watermark or
simply make it undetectable. They are aimed at distorting an embedded watermark
beyond recognition. An example for active attacks is copyright protection, fingerprinting
or copy control, etc.
b.) Passive attacks: The hacker efforts to identify whether there is a watermark and identify
it in passive type attacks. There is no destruction or deletion is done. These types of
attacks are important in covert communication.
c.) Forgery attacks: This type of attacks, hacker will not remove the watermark but inserts a
new valid watermark.
d.) Collusion attacks: This attack is imprecisely different from the active attacks. The
hacker uses various instances of the same information, containing each different mark,
to build a duplicate copy without any mark.
a.) Simple attacks: The other name of this attack is waveform attack and noise attack. This
is called as simple attacks because it tried to harm the embedded watermark by changing
the whole watermark without an attempt to recognize the single watermark. Some
examples of these attacks are filtering, addition of noise, waveform-based compression
(JPEG, MPEG) and gamma correction.
b.) Ambiguity attack: These attacks are trying to confuse by generating some fake
watermarked data or fake original data. Inversion attack is an example of this attack.
c.) Cryptographic attacks: The main target of this attack is breaking the security method in
watermarking techniques and found the mode to remove the inserted watermark infor-
mation. Due to high computational complexity, application of these attack is delimited.
d.) Removal attack: Without breaking the security of watermarking technique, a complete
removal of watermark data from the watermarked data [123]. There is no use of key in
watermark embedding. This technique holds denoising and quantization.
e.) Geometric attack: In reverse of removal attacks, these attacks do not actually remove the
inserted watermark itself, but aim to change the watermark detector synchronization with
the inserted information.
Further, our paper is summarized as follows: BSummary of state-of-the-art watermarking
schemes^Section presents the brief review of various interesting watermarking methods in spatial
and transform domain. Further, we have provided analysis of state-of-the-art watermarking is
tabulated in different form. Concluding remarks is presented in our BConclusion^section.
2 Summary of related research
Researchers have provided different robust watermarking approaches for the protection of
sensitive information in various emerging applications.
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A robust color image watermarking technique using decision tree induction in DCT domain
is developed [79]. The method uses DCT to transform the cover and watermark image first and
decision tree induction method uses to hide the secret watermark.
Reference [29] developed a feature-based image watermarking. By applying the affine
invariant points on image, synchronization error of image can be resolved. Experimental
demonstration is compared with several existing techniques [5,92,112] and shows presented
method is robust under several attacks.
Authors developed a watermarking algorithm using SVD and genetic algorithm [4]. The
method uses singular vector to embed the watermark into the cover. Further, GA technique
uses for enhance the performance of the proposed scheme.
The wavelet-based watermarking is presented in [65]. The method uses scaling factor to
modify the singular vector of cover image with watermark. Further, multi-objective particle
swarm optimization (MOPSO) is used to optimize the balance between conflicting factors of
watermarking. A robust - hybrid dual watermarking technique is discussed by Singh [99]. The
method uses three different transform domain techniques to hide two different watermarks and
provide robustness against attacks. Further, an encryption algorithm uses to save the execution
time and make it suitable for practical applications demanding secure, low complex and robust
watermarking. The experimental results are compared with one of the methods that is
developed by Singh et. al [103]. Further, NC values are compared with different techniques
[87,108,110]. A feature-based watermarking is developed by Tsai et al. [118]. The method
uses scale-adapted auto-correlation matrix and the Laplacian-of-Gaussian operation to deter-
mine the circular feature regions and balance the conflicting factors of watermarking. An
optimal selection process is described, expressed as a multidimensional knapsack problem
which is resolved by genetic algorithm-based heuristics. Further, the results demonstration
shows the method is secure and resistant to various attacks and also compared with different
techniques [16,92,112,127].
Support vector regression (SVR) based blind watermarking is proposed in [119]. The
embedding method uses DWT and SVD to hide watermark data into cover. Further, author
uses particle swarm optimization (PSO) to optimize the proposed method. Results discussion
show impressive enhancements in both transparency and robustness. For robustness the DSS
scheme is further compared with existing methods [10,22,61,70]. In [67], Authors proposed
a secure watermarking method through spread spectrum and transportation theory using gray
scale images. The author’s uses multiplicative embedding to provide acceptable robustness at
minimum distortion.
In order to this, Shen and Hsu developed a watermarking scheme using association rules
and vector quantization. Initially, the rules are determined for both 2D barcode and watermark
information. In the embedding process, the generated rules of the watermark information are
embedding into the association rules of the cover barcode information. The results have shown
that the scheme is secure and excellent embedding capacity [94].
A reversible and high capacity watermarking technique using rhombus pattern, sorting and
Histogram shift method is proposed by Sachnev et al. [88]. Initially, the cover media is divided
into two different sets and the payload information is embedding into both sets. The proposed
method is robust and imperceptible for different attacks. Discussed technique is compared with
existing methods and found to be superior to [44,57,115,116]methods.PeiandGuo
developed a pixel-based data-hiding approach [81]. The method uses error-diffused images
to hide the watermark data. The concepts of lookup table are using for fast recovery of the
hidden watermark (s). In addition, the method is tested for color images. Experimental
Multimedia Tools and Applications (2019) 78:8603–8633 8609
demonstrations are showing the method is robust for the printing and scanning attacks even at
high decoding rate and compared with existing method [35]. In [60], author proposed a data
perturbation method to authenticate the secret data in the original data and returns the
perturbed data back to the original one. The dataset used here for experiment is compared
with other authors [12,26,33]. The method uses adjustable weighting approach to estimate the
degree of trouble of the original data. Demonstrated results clearly indicate the method is
robust and secure at high payload.
In audio signals, direct sequence spread spectrum technique is applied for watermarking
operations [47]. The improved techniques focused on increased in robustness and impercep-
tiveness, averting desynchronization attacks, easing the removal of attacks and lastly main-
taining a covert communication with public audio channel. In [114], Author demonstrated a
secure method of watermarking through Chaotic based encryption. Security, robustness and
distortion of the algorithm are evaluated by standard performance metric and provide a
solution for medical data authentication. Proposed method shows that it attains superior results
compare to other approaches [99,134].
A modification of prediction errors (MPE) based watermarking scheme is proposed in [36].
In this method, pixel values are expected first then error values are acquired. The proposed
method is implemented in two parts first is embedding algorithm and second is extraction and
image restoration method. The PSNR value of stego image generated by MPE is greater than
48 dB. Results showed that the embedding capacity of MPE is achieved many times higher
than other techniques [44,76,116,117,133]. In [95], Authors uses the vector quantization and
data mining concepts to develop a watermarking scheme. The scheme estimates the associa-
tion rule of cover information and modify it with the association rule of secret watermark. The
method has good embedding capacity and robust for common attacks and compared with other
methods [128].
In [107], authors discussed a watermarking scheme using three different transform domain
techniques. The combination of transform domain method with Arnold makes it suitable for
copyright protection. Robustness of this technique is tested under different attacks. Further
results are compared with different existing methods [6,28,32,54,64]. This scheme can be
extended for video and audio processing.
In [77], the authors use the concepts of watermarking, 2D barcode and biometric to provide
the multilevel security of the method. The 2D barcode is considering as cover image and
biometrics traits are using as secret watermarks. The authors imperceptibly hide both water-
marks in to 2D barcode cover image at high matching score. Although the current results are
based on human vision system and better solution for the visual concept [122,124,125]. The
document integrity is provided through hologram-based watermarking in [19]. In this tech-
nique, hologram coding techniques based generated information is hiding into the personal
information written on an ID card. Different types of hologram along with advantages,
problems and important Issues of the Hologram Watermarks are also introduced in this paper.
In [9], Chang et al. proposed an improved embedding capacity watermarking method using
JPEG. The method uses least signification bitof the quantized DCT coefficients to impercep-
tibly hide the watermark at acceptable security level. In [43], author developed a watermarking
technique that works for three different datasets such as numeric, nonnumeric and strings
datasets. The author’s uses different machine learning methods to prove the method preserved
classification accuracy. Further, the method uses all available rows of data for watermarking to
provide security of the secret message. Robustness of discussed scheme is compared to other
existing technique [42]. A watermarking method combined with encryption algorithm which
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gives surety of a priori and a post priori protection in [7]. The method uses different schemes to
provide reliability of the watermark at very low distortion. An ICA based watermarking
approach is presented in [75]. Initially, the watermark were produced by applying the visual
mask and taken the transpose of the produced watermark. Authors hide both watermarks in to
the host image. Results showed that it is robust besides many attacks and compared with many
existing techniques [14,48,52,53,55,68,126]. In [58], Li and Yang proposed a fragile
watermarking system using 1-D neighbourhood-forming method. Author demonstrated that
the method achieved low computational complexity and security without including the
concepts of cryptography. Watermarking operations proceeds in zigzag order.
In [134], the method uses transform techniques to hide three different encoded watermarks
into original data. Further, use of neural network makes it more robust against attacks. The
result demonstration shows the method makes the balance between conflicting factors of
watermarking and suitable for medical data authentication. However, the NC value of pro-
posed scheme is compared with other existing methods [27,103]. In [15], Delaigle et.al.
discussed a watermarking approach using grey-scale pictures. The authors uses additive
method to imperceptibly hide watermark consist of binary sequences in to the picture. Results
showed that bit-error-rate is very low and Itis resistance against lossy compression. In [13]
author provided a watermarking technique for efficient sharing of medical information. The
method uses knowledge digest (KD) to retrieve images and update databases through noisy
channel. The method also spreads to reversible scheme and pertained it to compressed images
such as JPEG. Authors proposed a watermarking scheme for medical data authentication
[113]. The method improved the robustness through combination of DWT-SVD and error
correction code. Further, author uses ‘U’component of SVD for watermark embedding to
make it free from false positive problem. Further, the experimental results are compared with
other existing methods [78,104]. Various experimental analysis shows the method is appro-
priate for medical applications.
The author presented a robust watermarking scheme that is transforming the length of
vector that match a vertex to the centre of the model via vertex scrambling [85]. Results have
shown this watermark is robust against attacks like mesh applications, an accumulation of
noise and model cropping. In [8], authors developed a secure watermarking through spread-
spectrum. The method uses a look-up-table (LUT) to imperceptibly hide spread-spectrum
watermark. The authors demonstrated that the proposed detection algorithm is efficiently fast
then traditional systems. A 3Dwatermarking algorithm in which watermark is embedded into
normal vector distributions of every patch is proposed in [56]. This technique is robust against
many attacks. The method does not require actual model to separate the watermark. A secure
multilevel watermarking is provided through wavelet and spread spectrumin [102]. The
method uses three different types of encrypted text watermarks and embedded in wavelet
coefficient of the medical cover. The results discussion show that the method is resistant to
various attacks while addresses the health data management issues. In [74], a quadratic
programming (QP) framework for watermarking to maintain the imperceptibility-robustness
trade-off is introduced. The method uses spread transform to the baseline QP system alter the
watermark embedding requirements. The robustness is increased up-to a limit where robust-
ness functioning cracked-up quicker than the baseline system.
In [1], Ali and Ahn proposed a watermarking algorithm in wavelet domain. The method
uses cuckoo search to determine the optimal scaling factors for better imperceptibility and
robustness performance. The extensive analysis of the results and discussion show the method
is efficient for various applications.
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The method presented in [62], author introduced a H.264/AVC based watermarking using
BCH and DCT. The method encodes the secret information by BCH code before data hiding
and encoded watermark hide into DCT coefficients. The tested method provides high robust-
ness and embedding payload at acceptable quality of the watermarked image and compared
with other technique [63].
In [105], authors proposed three transform domain techniques based multiple watermarking
approach for practical applications. The robustness and security achieved through ECCs and
selective encryption, respectively. In addition, BPNN is applied on recovered image watermark
to provide improved robustness. Although the NC values of proposed scheme are compared
with other existing methods [30,90,97,131,132,135]. The results demonstrated in various
ways and found suitable for the application demanding robust, secure and low complexity
watermarking approaches. In [73], Najih et al. proposed a watermarking method using discrete
contourlet transform (CT) and quantization index module. The cover image is transform by CT
and coordinate points are computed for some selected coefficients. The method hides water-
mark data in to angle ratio of all image portions. Further the method uses Lagrange scheme to
minimize the watermarked distortion. Algorithm attains better transparency and good robust-
ness. Robustness of proposed scheme is compared with another existing scheme [24,66].
Kumar et al. [51] provided a watermarking technique in wavelet domain. The method
combining different transform techniques to hide watermark information into original form
of data. The method uses Arnold transform to scramble the image watermark before hide into
cover data. Further, watermarked image is compressed by set partitioning in hierarchical tree
(SPIHT) scheme to provide better performance of the proposed scheme. Results demonstration
proved the efficiency of the watermark system. Further, the proposed scheme is compared with
other similar techniques [34,46,89,91,98,101]. The author developed a low complexity and
memory requirement watermarking method using combination of LWT-QR and LSVR [69].
The cover image is transformed by LWT and low frequency sub-band is selected for embed-
ding. This frequency sub-band is divided into sub-block. The method uses QR code to
decompose each sub-block. The significant components of first row of ‘R’matrix is using
for embedding the watermark data. Results have shown that algorithm is high proficient
robustness and imperceptibility. Although the results of discussed scheme are compared with
other existing methods [72,82,109]. Duy et al. proposed machine learning based
watermarking method for EEG data in DWT domain [20]. The method uses intelligent
learning machine for recover the watermark to save memory for storing both data (EEG and
watermark). Author demonstrated that the method is resistant to various types of attacks.
Results are also compared with another discussed methods [39,40,83].
In [59], author produced a robust watermarking scheme through Quaternion Hadamard
Transform (QHT) and Schur decomposition. The method hide binary watermark into color
image. Author uses various performance metrics to evaluate the performance of the proposed
scheme. The experimental result shown that presented technique is not only imperceptible bust
also robust against many attacks. Robustness of the discussed method is compared with other
existing method [111].
The medical data authentication through watermarking is provided by Shehab et al.
[93]. The host image is first divided into blocks. The selected block is scrambled by
Arnold and the block with zero LSB is transformed by SVD. The method determines
some bits (using the blocks) to hide into the LSB of the original data. The method is
found suitable for copy and pest attack, text addition, content removal and VQ attacks.
Results have shown the superiority of the method with other state-of-the-art [18,21,80,
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Table 2 Summary of some potential methods
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
[79] Data mining based
robust watermarking
NA DCT, decision tree
induction,
Max PSNR = 50.8609
(Red Component)
Total no. of instances:
20480
512 × 512/32 × 32
[29] Improved feature-based
robust watermarking
NA graph theoretical
clustering
algorithm, affine
covariant regions,
indirect inverse
normalization
PSNR = greater than
40 dB
−100 images are using
for experiments.
-Determine the
detection ratio
against attacks.
-Computationally high
512 × 512 / 256-length
[4] Imperceptible and
Robust watermarking
NA SVD, GA Results are obtained at
various mutation
rates, Crossover
rate, Population
size etc.
-Number of
generations are 400
for each
experiment.
- GA run 30 times
with different
populations
256 × 256/32 × 32
[65] Imperceptible and Robust
watermarking
Blind LWT, SVD,
multi−objective
particle swarm
optimization
-PSNR = 54.907 dB
for Boat Image
-NC = 1
- Used multiple
scaling factors
256 × 256/32 × 32
[99] Low complex and Robust
watermarking using
encryption
Non-blind DWT, DCT, SVD and
encryption
-PSNR = 28.51 dB at
gain factor 0.01
- NC = 1 at gain factor
0.1
-Resistant to various
attacks
-Increased
computational
complexity
−512 × 512/512 × 512
- text watermark is of
185 characters
[118] Improvement in
robustness and security
through feature-based
watermarking
Blind noise visibility
function
PSNR = 42.21 dB
(Pepper)
-detection ratio = 0.46
(Linear)
-BER = 0.34 (Aspect
Ratio Change)
-Using UCID database
Noise visibility
function is used to
fix the embedding
strength
512 × 512/ Generated
watermark length
of 256 and repeated
16 times
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Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
- Repeatability
ratio = 0.77 (JPEG
50)
[119] Imperceptible and Robust
watermarking
Blind DWT, SVD, SVR,
PSO
-PSNR>35 dB
-NC = 0.988
-PSO is used to
optimize the
scheme
- scale factor is set to
0.03
-Robust to other
techniques
512 × 512/32 × 32
[67] transportation theory
based Secure
watermarking
NA Spread-Spectrum, 9/7
Daubechies DWT
-PSNR = 44.05 dB
(Average)
-BER = 6.268750e-02
(at average PSNR)
- multiplicative
embedding
-Using 2, 000 images
to performed
experiments
512 × 512
combinations
[94] Data authentication
through watermarking
and 2D barcode
Blind Association rules
(AR) with the Vec-
tor quantization
(VQ)
PSNR = 31.62
Method
output = 60.62%
Key is using as
personal password
2D barcode
900 × 1782
grey-scale image
Watermark 480 × 360
grey-scale
[88] High capacity with less
distortion-based
watermarking
NA Watermarking
procedure with
Rhombus,
histogram shift and
Sorting Technique
Payload closed to
0.5b/pixel
- significant
improvements over
other methods
512 × 512/−
[81] Robust watermarking for
Halftone images
NA kernels-alternated
error diffusion,
Euclidean distance
lookup table, dot
and error diffusion,
ordered dither
Decoding rate = 95.77
(for 11 × 11
decoding region)
Low Computational
complexity.
-Excellent embedding
capacity
512 × 512/32 × 32
[60]Blind NA
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Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
Watermarking in data
mining environments
decision tree, native
bytes and support
vector machine
Payload for
RDT = 9,000,000
(KDD cup)
PDE = 6,000,000
(KDD cup)
- execution time,
memory space and
watermark payload
are evaluated and
compared
[47] Robust and secure
watermarking for
audio signal
NA Spread spectrum,
modulated complex
lapped transform
Wat ermar k
detection = 0.0761
(copy sample)
-Robust for many
attacks
- Secure for estimation
attack
NA
[114] Secure watermarking Semi-blind NSCT, Chaotic,
RDWT and SVD
NCs > 0.7,
NPCR>0.99
UACI>0.32
PSNR>35 dB(in most
of the cases)
-Nine different types
of cover images are
considered
-Superior than others
512 × 512/256 × 256
and 128 × 128
[36] Imperceptible and high
capacity watermarking
Blind Using the concepts of
histogram-shifting
technique, median
edge detection
PSNR = 49.12 dB (for
c)
Increased
payload% = 487
- histogram of
prediction errors is
modified
512 × 512/Can embed
138,327 bits
[95] Robust watermarking
through data mining
Semi-blind Vector Quantization
and Association
Rules
PSNR and NC are
tested at different
threshold
- Method can embed
more than the size
of cover
-Threshold is defined
-Reduce the false
judgement rate
512 × 512/various
sizes
[107]Watermarkingfor
copyright protection
Blind Transform techniques
and Arnolds
PSNR = 52.34 dB
(Lena)
NC = 0.9785 (Lena)
-Free from false
positive problem 1024 × 1024/128 ×
128
[77] Secure watermarking Blind Determine the
embedding
location, selection
of threshold
PSNR = 86.47(2D
barcode),
64.33(Face-
image),
-capable of detecting
attacks
-Barcode and
biometric images
are used
Barcode with some
height and
width/Size of the
face and fingerprint
images are
Multimedia Tools and Applications (2019) 78:8603–8633 8615
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
58.87(Finger-print
image)
-Tested the method
with different
metrics
240 × 320 and
300 × 300, respec-
tively
[19] Secure watermarking Fragile Hologram techniques NA Helps to prevent ID
card forgery
NA
[9] Watermarking with high
capacity
NA DCT, LSB PSNR = 39.14 dB
(Girl)
Compared with JPEG
hiding-tool
256 × 256/−
[43] Secure watermarking for
Outsourced Datasets
Blind Feature Ranking,
Threshold
Computation, Data
grouping, machine
learning
approaches
Better decoding
accuracies
Uses 25 different
datasets for
experimental
purpose
NA/Watermark
length = 16 bits
[7] High imperceptible
medical image
watermarking
Fragile AES, substitutive
watermarking
algorithm, QIM
PSNR is greater than
60 dB
-provides Image
integrity
100 × 100ultrasound
images of
576 × 688 pixels
[75] Robust watermarking Blind ICA PSNR = 43.99 dB
(Expt2)
Transformed image
also considered as
watermark
512 × 512/64 × 64
(Expt2)
[58] Secure watermarking at
low complexity
Blind 1-D neighborhood PSNR = 51 dB. Applicable for
important
applications
256 × 256/−
[134] Robust, imperceptible
and secure
watermarking for
7identity
authentication
NA DWT, DCT, SVD
with BPNN and
Arnold Transform
-PSNR = 43.88 dB
(0.01)
-BER = 0 (for
signature)
-NC = 0.9861 (0.08
without BPNN)
-NC = 0.9888 (0.08
without BPNN)
-Useful for prevention
of patient identity
theft
512 × 512/256 × 256
and 190 characters
[15] Robust watermarking on
Human visual model
NA Bit error rate is 3.0 2220possibilities to
choose the
NA
8616 Multimedia Tools and Applications (2019) 78:8603–8633
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
Additive
watermarking
technique
embedding
parameters
[13] Imperceptible
Wat er marki ng f or
medical application
Blind knowledge digest PSNR = 41.7 dB Tested for
JPEG-compressed
images
- consider 750 images
for testing
365 × 378 pixels/
2373 bits
[113] Robust, imperceptible
and secure
watermarking
Blind DWT-SVD PSNR = dB
(X-ray2)BER = 0
-Also robust for
Checkmark attack
1024 × 1024/32 × 32
and 1022 bits
[85] Robust watermarking for
3D objects
NA Vertex Scrambling Correlation value = 1 - 2955 vertices and
5870 triangle faces
are present in the
mesh
-Tested for different
attacks
NA/50 bits
[8] Secure watermarking blind Look-up table,
SPREAD
SPECTRUM
Circulation LUT
correlation = 8.3 ×
109
-fast detection
algorithm
NA
[56] Imperceptible and robust
watermarking for 3D
polygonal
NA vector distribution,
Patch Classification
and Extended
Gaussian Image
(EGI)
Bit error = 0 (random
noise, cropping)
-Stanford bunny
model is used
1-bit watermark with
50 length
[102] Provide authentication for
medical data through
watermarking
NA DWT, Spread
Spectrum
PSNR = 40.02 dB and
BER = 0.1538
Achieved two level of
security
512 × 512/−
[74] 3D watermarking NA Spread spectrum
transform and
quadratic
programming (QP)
distortion close to
0.37, BER and
RMS is also
determined
-database of 10
meshes between
20 k and 100 k
vertices
NA
Multimedia Tools and Applications (2019) 78:8603–8633 8617
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
[1] Robust and imperceptible
watermarking
NA DWT and Cuckoo
search
-PSNR = 38.0358
(both sizes of
watermark)
-NC = 0.9613 (Pepper
at 1stlevel
decomposition)
-NC = 1 (Baboon,
Lena, Pepper at 2nd
level
decomposition)
Balance between
conflicting factors
of watermarking
256 × 256/
128 × 128,64 × 64
[62] Robust and imperceptible
watermarking
blind BCH code,
H.264/AVC, DCT
-PSNR = 55.45 dB
-bit rate increases
1.06%
-Utilized to avert the
distortion drift
- video sequences are
encoded to test the
sample
I-frames/different no.
of bits
[105] Dual watermarking Non-blind DWT, DCT, SVD,
BPNN and
selective
encryption, error
correction
Max
PSNR = 34.88 dB
NC = 0.9965 (0.1)
BER = 0
-Provide a solution to
protect social
network data
512 × 512/128 × 128,
100 characters of
text watermark
[2] Robust watermarking Blind DWT and PNN PSNR = 68.27 dB and
NCC = 0.9779
-Performed better than
others
512 × 512/64 × 64
[73] Robust and imperceptible
watermarking
Fragile discrete contourlet
transform and QIM
-PSNR = 61.9914
-NCC = 1 (without
attack)
-Lagrange method is
pertained for
optimization
-High transparency
512 × 512/−
[51] Robust and secure
watermarking
NA DWT, DCT, SVD,
SPIHT and Arnold
Transform
-PSNR = 34.68 dB
(MRI)
-NC = 0.9973
(Barbara)
-SSIM = 0.995857
-SPIHT encoding
results in
compressed
watermarked
image.
512 × 512/256 × 256
[69]NA 512 × 512/32 × 32
8618 Multimedia Tools and Applications (2019) 78:8603–8633
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
Wat er marki ng f or
copyright protection
LWT, Q R
Decomposition and
LSVR, Arnold
transform
-PSNR = 45.9283 dB
(Lena)
-NC = 1(without
attack, Filtering,
scaling)
-BER = 0
Less computational
cost and memory
constraint
[20]Watermarkingfor
outsourced EEG data
Blind DWT, support vector
data description,
Chaotic encryption
-PSNR = 66.55 (Avg)
-NC = 1
-BER = 0
-error analysis = 0.97
It achieves good
imperceptibility
and strong
robustness
NA/32 × 32
[59] Robust and imperceptible
watermarking for color
images
NA Quaternion Hadamard
transform, Schur
decomposition
-SSIM = 0.9917
(Lena)
-NC=1(Lena,
Pepper, Baboon in
no attack)
-NC = 1 (Lena) in
Gamma Correction
and Brighten
-Complexity is lower
than RGB channels
512×512×24/
64 × 64 × 2
[93] Secure and robust
watermarking for
medical images
Fragile Singular value
decomposition and
Arnold Transform
-PSNR = 38.96 dB
and tamper
localization is
99.56% (Copy and
Paste type 1) for
image plane
-NCC1= 0.9999
(content removal,
copy and paste
attack 1 for image
kidney)
-NCC
2=0.9985
(copy and paste
−12 grayscale medical
images are used
- highly reliable
512 × 512/−
Multimedia Tools and Applications (2019) 78:8603–8633 8619
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
attack 2 for image
Liver)
-FPR = 0.89 (VQ
attack)
-FNR=0.06(copy
and paste attack 1
for image Liver)
[38]Watermarkingfor
ownership rights
blind Z language, hashing
and permutation
Accuracy rate = 100% - using Badge dataset
-It handles big data
-It guarantees original
data recovery after
watermark
decoding
−/8 bit
[37] Robust Watermarking for
relational data
Semi-Blind Watermarking
encoding and
decoding through
Genetic Algorithm
- Max mean and
variance = 55.019
and 81.697,
respectively
Number of
generations are 100
Population size is 50
-Result analysis at
different datasets
More than 300 tuples
[3] Robust watermarking for
grayscale images
NA Adaptive logo
texturization via
ALT- MA RK
PSNR = 44.21 dB
(Peppers)
NC = 0.979 (Mandrill)
Technique is efficient
in terms of
computational
speed
512 × 512/64 × 64
[11] Robust watermarking for
medical images
NA DWT, Mexican Hat
wavelet,
spread-spectrum
-Max
PSNR = 43.9986
dB
-Max NC
value = 0.9953
WDR = 20 dB
-Used Cauchy
statistical model
1024 × 1024/50 × 9
[137] Robust watermarking for
Copyright protection
NA DWT, APDCBT,
SVD, DC
coefficients
PSNR = 101.97 dB
NCC = 0.9724
-APDCBT is
introduced by
combining DWT
and SVD
512 × 512/32 × 32
8620 Multimedia Tools and Applications (2019) 78:8603–8633
Table 2 (continued)
Ref. ID Author Objective (s) Watermark system Used techniques Results Remarks Cover size/ watermark
(s) size
[136] Robust and imperceptible
watermarking
Non-blind DWT, SVD with
GDPSO
-PSNR = 36.877771
&Fitness
Value = 1.977786
(using GDPSO)
- PSNR = 39.792252
&Fitness
Value = 1.984123
(using DWT-SVD
with GDPSO)
-GDPSO gives better
results than other
alternates of PSO
-presented technique
is applied for
different attacks
also
512 × 512/512 × 512
Multimedia Tools and Applications (2019) 78:8603–8633 8621
Table 3 Analysis on Different Factors Used for Image Watermarking
Reference Number (Study ID)
Factors [79][29][4][65][99][118][119][67][94][88][81][60][47][114][36][95]
Robustness ✓×✓✓ ✓✓ ✓ ✓××✓×✓××✓
Imperceptibility ✓××✓×× × ×× ××× ✓×××
Security × ××× ×✓× ×× ××× ×✓×✓
Embedding Capacity × × × × ✓×××✓××× ×× ✓×
Accuracy × ××× ×× × ×× ××× ×× ××
Efficiency × ××× ×× × ×× ××× ×× ××
Transparency × × ✓×××✓×× ××× ×× ××
Distortions × ✓×× ×× × ×× ✓×× ×× ××
Payload × × × × × × × × × × × ✓×× ××
Computationalcomplexity× ××× ×× × ×× ××× ×× ××
Biterrorrate × ××× ×× × ×× ××× ×× ××
Preserves image quality × × × × × × × × × × × × × × × ×
Reference Number (Study ID)
Factors [107][77][19][9][43][7][75][58][134][15][13][113][85][8][56][102]
Robustness ✓××× ×× ✓×✓××✓✓×✓✓
Imperceptibility ✓××× ×× × ×✓××✓×× ✓×
Security ✓✓✓×✓××✓✓ ××✓×× ×✓
Embedding Capacity × × × ✓×× × ×× ××× ×✓××
Accuracy × ××× ×× × ×× ××× ×× ××
Efficiency × ××× ×× × ×× ××× ×× ××
Transparency × ××× ×× × ×× ××× ×× ××
Distortions × × × × × ✓× ×× ××× ×× ××
Payload × ××× ×× × ×× ××× ×× ××
Computationalcomplexity× ××× ×× × ✓× ××× ×× ××
Bit error rate × × × × × × × × × ✓×× ×× ××
Preserves image quality × × × × × × × × × × ✓×××××
Reference Number (Study ID)
Factors [74][1][62][105][2][73][51][69][20][59][93][38][37][3][11][137]
Robustness ✓ ✓✓✓ ✓✓ ✓ ✓✓ ✓✓✓ ✓✓ ✓✓
Imperceptibility × ✓×× ✓✓ ×✓✓ ✓×× ×✓××
Security × × × ✓×× ✓×× ×✓×××××
EmbeddingCapacity × ××× ×× × ×× ××× ×× ××
Accuracy × ××× ×× × ×× ××× ×× ××
8622 Multimedia Tools and Applications (2019) 78:8603–8633
Table 3 (continued)
Efficiency × ××× ×× × ×× ××× ×× ××
Transparency × ××× ×× × ×× ××× ×× ××
Distortions × ××× ×× × ×× ××× ×× ××
Payload × ××× ×× × ×× ××× ×× ××
Computationalcomplexity× ××× ×× × ×× ××× ×× ××
Biterrorrate × ××× ×× × ×× ××× ×× ××
Preserves image quality × × × × × × × × × × × × × × × ×
Factors [136] *******
Robustness ✓
Imperceptibility ✓
Security ×
Embedding Capacity ×
Accuracy ×
Efficiency ×
Transparency ×
Distortions ×
Payload ×
Computational complexity ×
Bit error rate ×
Preserves image quality ×
Multimedia Tools and Applications (2019) 78:8603–8633 8623
Table 4 Analysis on Different Techniques Used for Image Watermarking
Tec hni que s St udy I Ds
Discrete Cosine Transform (DCT) [79][107][105][51]
Singular Value Decomposition (SVD) [4][65][119 ][105][51][69][93][114]
[136]
Discrete Wavelet Transform (DWT) [119][102][1][105][2][51][136]
Discrete Contourlet Transform [73]
Discrete Fourier Transform (DFT) [47]
NSCT-RDWT-SVD-Chaotic [114]
Lifting Wavelet Transform (LWT) [65][69]
Quantized Discrete Cosine Transforms [9]
RDT Algorithm [60]
Independent Component Analysis (ICA) [75]
Reversed Watermarking [13][37,38]
Watermarking Encoding and Decoding [47][36][43][75][85][56][20][37]
Additive watermarking [15]
Vertice Scrambling [85]
Patch Classification, Extended Gaussian Image [65][56]
QR Decomposition [65][56][69]
Hybrid Techniques (DWT-SVD, DCT-SVD) (DWT-DCT-SVD) [99][107][134][113][137]
Spread Spectrum [67][8][102][74]
Transportation Theory [67]
QIM (Quantization Index Modulation) [7][73]
Z-notation [38]
Adaptive logo texturization via ALT-MARK [3]
Mexican Hat [11]
Set Partitioning in Hierarchical Trees (SPIHT) [51]
Decision Tree ID3 [79][60]
Genetic Algorithm [4][118][105][37]
Association Rules [94][95]
Vector Quantization [94][95]
Langrangian Support Vector Regression (LSVR) [65][56][69]
Probabilistic Neural Network (PNN) [2]
Back Propagation Neural Network (BPNN) [134][105]
Particle Swarm Optimization (PSO) [119]
Support Vector Regression (SVR) [119]
Graph theoretical clustering algorithm,
Affine covariant regions, Indirect inverse normalization
[29]
Modulated Complex Lapped Transform (MCLT) [47]
Native Bayes [60]
Support Vector Machine [60]
Image restoration algorithm [36]
Digital watermarking, Threshold selection [77]
Hologram techniques [19]
Block Cipher Algorithm [7]
Watermark embedding and extraction with Zigzag [58]
Look -Up Table [8]
Quadratic Programming (QP) [74]
BCH Syndrome Code [62]
Block based and hybrid pixel based digital watermarking [81]
Watermarking procedure with Rhombus, histogram shift and Sorting
Technique
[88]
Arnold Cat Map Encryption [107]
Cuckoo search [1]
Arnold Transform [134][51][93]
Quaternion Hadamard transform and Schur Decomposition [59]
Encryption [99]
Noise Visibility Function [118]
8624 Multimedia Tools and Applications (2019) 78:8603–8633
84]. In [38], Author discussed a watermarking scheme for social network data. The result
analysis shown the method is suitable for authentication of digital data. Iftikhar et al.
discussed a robust as and semi-blind reversible watermarking scheme using numerical
relational data [37]. Watermarking approach with the genetic algorithm is used to achieve
the objective of the proposed system. Results have shown that the effectiveness of the
method against malignant attacks. The author [3] presented a robust gray-scale
watermarking method that performs through adaptive texturization of the logo (ALT-
MARK). The method uses Arnold Transform to not only secure, but also texturize the
logo for improve robustness against attacks and hide the watermark data in DWT
domain. The performance analysis shows the method is better than other similar com-
peting schemes [25,28,41,86,129].
Chauhan et al. [11] presented an adaptive watermarking scheme and its detection for
medical images. The method hides Pseudo-Noise into the selected coefficients of DWT.
Author uses the statistical property of wavelet coefficients of watermarked image and prob-
ability distribution function (pdf) was utilized for designing the watermark recovery/detection
purpose. Result established that the method is superior to other competing approaches [104].
Author developed a Robust watermarking through DWT, all phase discrete cosine
biorthogonal transform (APDCBT) and SVD [137]. The host image is transformed by DWT
and selected sub-bands are using for embedding the two similar watermarks. Due to excellent
energy concentration, author uses APDCBT to provide better protection of secret data
(watermarks). Further, imperceptibility is improved by using DC coefficients is employed.
Table 4 (continued)
Tec hni que s St udy I Ds
9/7 Daubechies DWT [67]
GDPSO (Guided Dynamic Particle Swarm Optimization) [136]
Fig. 5 Identified techniques Used to improve the robustness of watermarking techniques
Multimedia Tools and Applications (2019) 78:8603–8633 8625
Result discussion is compared with other existing methods [23,64] and found to be suitable
for copyright protection.
Author discussed the algorithm Guided Dynamic-PSO (GDPSO) positively attained two
targets that is avoid stuckness and premature convergence [136]. These two are the problem of
PSO. GDPSO found the most suitable watermark asset in terms of DWT-SVD based image
watermarking scheme. Experimental study shows that it achieves the superior imperceptibility
and robustness and compared with another watermarking schemes [17,31,45]. It is also
appropriate in scaling factor for host and watermark images.
The summary of the above discussed techniques is provided in Table 2.
Based on the above extensive discussion, we have identified robustness,
imperceptibility, security and capacity are the major factors in most of the research papers.
Further, researchers are using various techniques for improving/balancing these concerns to
make the efficient watermarking system. Our study identified major factors and novel
techniques used by the potential researchers are shown in Tables 3and 4, respectively. In
Fig. 5, we have identified different efficient schemes/solutions to improve the robustness of
watermarking techniques. From Table 5,itisanalysedthatmostofthestudieshavetakenthe
size of image and size of watermark of variable length. In very few studies standard image
size of 512 × 512 pixels is mapped with 32 × 32, 64 × 64 and 128 × 128pixel size of
watermark. It is suggested that researcher must compare their results using the same size
of image with respect to same size of watermark.
3 Conclusion
In this paper, we have reviewed various robust and imperceptible watermarking methods in
spatial and transform domain. We have discussed basic concepts of digital watermarking,
important characteristics and applications of watermark in detail. Further, various analysis,
research challenges, identified solutions and comparison of different notable robust, imper-
ceptible and computationally efficient watermarking techniques are tabulated. Authors believe
that provided information in this paper will useful for active researchers to implement an
efficient watermarking system. In future, the performance comparison of other multimedia
watermarking techniques can discuss.
Table 5 Detail of Size of Cover Object and Size of Watermark Used in Different Studies on Image
Wat er marki ng
Size of
image
4×4
Pixels
32 × 32
Pixels
64× 64
Pixels
128 × 128
Pixels
256 × 256
Pixels
512 × 512 Pixels 1024 × 1024
Pixels
Size of
Wat er mark
4×4 –– – – – – –
32 × 32 –– – – [4,65][79][119][81]
[69][137]
[113]
64 × 64 –– – – [1][75][2][3]–
128 × 128 –– – [114 ][1][105][107]
256 × 256 –– – – [114][134][51]–
512 × 512 –– – – – [99][114][136]–
1024 × 1024 –– – – – – –
8626 Multimedia Tools and Applications (2019) 78:8603–8633
Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
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Multimedia Tools and Applications (2019) 78:8603–8633 8631
Namita Agarwal is currently pursuing Ph.D. from Jaypee University of Information Technology, Waknaghat,
Solan (H.P). Her research interest includes Data Hiding Techniques and Image Processing.
Amit Kumar Singh received the bachelor’s degree in computer science and engineering from the Institute of
Engineering, Veer Bahadur Singh Purvanchal University, Jaunpur, India, in 2005, the M.Tech. degree in
computer science and engineering from the Jaypee University of Information Technology, Waknaghat, India,
in 2010, and the Ph.D. degree in computer engineering from the National Institute of Technology, Kurukshetra,
India, in 2015. He was with the Computer Science and Engineering Department, Jaypee University of
Information Technology, from 2008 to 2018. He is currently an Assistant Professor with the Computer Science
and Engineering Department, National Institute of Technology at Patna (An Institute of National Importance),
Patna, India. He has authored over 80 peer-reviewed journal, conference publications, and book chapters. He has
authored two books entitled Medical Image Watermarking: Techniques and Applications, in 2017, and Animal
Biometrics: Techniques and Applications, in 2018 (Springer International Publishing). He has also edited the
book Security in Smart Cities: Models, Applications, and Challenges (Springer International Publishing, 2019),
the Proceedings of 4th IEEE International Conference on Parallel, Distributed and Grid Computing in 2016 and
the Proceedings of 4th International Conference on Image Information Processing in 2017. He currently serves
on the Editorial Board of two peer-reviewed international journals, including the IEEE ACCESS and Multimedia
Tools and Applications (Springer). He has edited various international journal special issues as a Guest Editor,
such as IEEE Consumer Electronics Magazine, IEEE Access, Multimedia Tools and Applications, Springer,
International Journal of Information Management, Elsevier, Journal of Ambient Intelligence and Humanized
Computing, Springer, Int. J. of Information and Computer Security, InderScience, International Journal of Grid
and Utility Computing, Inderscience and Journal of Intelligent Systems, Walter de Gruyter GmbH & Co. KG,
Germany. His research interests include data hiding, biometrics, & Cryptography
8632 Multimedia Tools and Applications (2019) 78:8603–8633
Pradeep Kumar Singh is currently working as Assistant Professor (Senior Grade) in Department of Computer
Science & Engineering at Jaypee University of Information Technology (JUIT), Waknaghat, H.P. He is having
10 years of vast experience in academics at reputed colleges and universities of India. He has total 215 google
scholar citations in his account. He has published 52 research papers in various conferences and Journal s of
Repute including the IEEE, Springer. He is senior member ACM & Computer Society of India (CSI-India). Dr.
Singh has edited various special issues in different Journals including the IJSSE, IGI Global USA and JTEC
Malaysia. He is reviewers of various SCI, Scopus Journals including IEEE Transactions on Industrial Informat-
ics, IEEE Communication Magazine, JST Malaysia and Various Scopus Journals from IGI Global USA. He has
received three research grants in his account from State Govt. and DST-India along with one consultancy.
Multimedia Tools and Applications (2019) 78:8603–8633 8633
