=Paper=
{{Paper
|id=Vol-2391/paper50
|storemode=property
|title=Watermarking algorithms for JPEG 2000 lossy compressed images
|pdfUrl=https://ceur-ws.org/Vol-2391/paper50.pdf
|volume=Vol-2391
|authors=Victor Fedoseev,Tatiana Androsova
}}
==Watermarking algorithms for JPEG 2000 lossy compressed images ==
https://ceur-ws.org/Vol-2391/paper50.pdf
Watermarking algorithms for JPEG 2000 lossy compressed
images
V Fedoseev1,2, T Androsova1
1
Samara National Research University, Moskovskoe Shosse 34Π, Samara, Russia, 443086
2
Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and
Photonics" RAS, Molodogvardejskaya street 151, Samara, Russia, 443001
e-mail: vicanfed@gmail.com
Abstract. In the paper, we propose two watermarking algorithms for semi-fragile data hiding
in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of
quantization index modulation. These methods have a property of semi-fragility to the image
quality. It means that the hidden information is preserved after high-quality compression, and
is destroyed in the case of significant degradation. Experimental investigations confirm this
property for both algorithms. They also show that the introduced embedding distortions in
terms of PSNR and PSNR-HVS are in almost linear dependence on the quantization parameter.
It allows us to control the quality at an acceptable level when information embedding.
1. Introduction
The JPEG 2000 image compression format, despite being less popular compared to JPEG, can
provide better compression and is therefore widely used in remote sensing systems, medical
imaging, and some other areas [1]. This fact underscores the importance of the task of protecting
JPEG 2000 images from unauthorized changes. For example, the recipient of remote sensing data must
have confidence in the absence of their falsification, as well as the doctor who makes the diagnosis
based on the digital image must be convinced of its authenticity and in the absence of distortions
caused by lossy data compression.
One of the common approaches for the problem of protecting images from changes is embedding
of semi-fragile digital watermarks, which are preserved in images while minor changing and
destroyed after significant modifications. However, only a small number of semi-fragile
watermarking methods for JPEG 2000 can be found in the literature [2]-[4]. Specifically, such class
of methods includes one by Sun et al. [2] based on the EBCOT encoding procedure and the two
algorithms by Maeno et al. [3], which do not allow to control quality factor. One more algorithm by
Preda [4] is not linked with JPEG 2000 parameters. In this paper, we propose such a method for
lossy JPEG 2000 compression mode, based on the quantization index modulation technique (QIM)
[5].
The paper is organized as follows. In Section 2, we briefly describe the quantization procedure
specified in the JPEG 2000 lossy compression standard. Section 3 presents the
developed watermarking method while Section 4 investigates it.
V International Conference on "Information Technology and Nanotechnology" (ITNT-2019)
Image Processing and Earth Remote Sensing
V Fedoseev, T Androsova
2. JPEG 2000 lossy compression procedure
The flowchart of the compression algorithm is shown in Figure 1. At the first stage of compression,
the brightness of each component is reduced by 128 [6]. Then the image color space is converted from
RGB to YCbCr. The resulting image is subjected to discrete wavelet transform (DWT) with the
Daubechies filter bank (9, 7) for the partition of the image into low-frequency and high-frequency
areas (subbands), also called as the approximation and the details [6].
Figure 1. JPEG 2000 coding flowchart.
After the transformation, each coefficient ππ (π’, π£) of subband π is quantized by the formula:
ππ (π’, π£)
ππ (π’, π£) = οΏ½οΏ½ οΏ½οΏ½ β π πππ(ππ (π’, π£)), (1)
βπ
where ab (u, v) are quadrant coefficients and βb is the quantization step.
The quantization step is represented by two bytes: 11-bit mantissa Β΅b and 5-bit exponent Ξ΅b and is
determined by the following formula:
ππ
βπ = 2π
π βππ οΏ½1 + 11 οΏ½, (2)
2
where π
π is the nominal dynamic range of subband π.
According to [7, 8], two modes of calculating the values βπ for various π are possible, which are
expounded quantization and derived quantization. In the first mode, the values (πππ , ππ ) are explicitly
transmitted by the way similar to q-table in JPEG coding. In the second mode, which is considered in
this paper, (πππ , ππ ) values are calculated from the given values (ππ0 , π0 ) β (ππ, π), defined for the LL-
subband, using the following equations:
πππ = ππ β ππΏ + ππ ; ππ = π, (3)
where ππΏ is the total number of decomposition levels and ππ is the level number corresponding to
subband π [8].
The final step of the compression process is the error-free coding of quantized coefficients using
the arithmetic coding based on bit-planes. The JPEG 2000 decoder reverses the given operations.
3. Embedding information based on QIM
To embed the watermark, we modified the quantization operation (1) according to the QIM concept.
Specifically, we used two forms of QIM embedding rules: Simple-QIM [9]
π₯(π)
π¦(π) = 2β οΏ½ οΏ½ + β β π(π), (4)
2β
where π₯(π) are quantized values, and π(π) are the embedded bits, and DM-QIM (Dither Modulation
β Quantization Index Modulation) [5]. The latter one assumes the use of two dither vectors
π0 (π), π1 (π) that are consistent with each other and used when embedding bits β0β and β1β:
β β
π0 (π), π1 (π)π οΏ½β ; β 1οΏ½ , π β [0, πΎ β 1],
2 2
where πΎ is the number of quantized values. Information embedding in DM-QIM is carried out as
follows:
π₯(π) + ππ(π) (π)
π¦(π) = β β
πππ’ππ οΏ½ οΏ½ β ππ(π) (π). (5)
β
To use (4)-(5) in our adaptations for JPEG 2000, the embedded watermark should be robust against
the JPEG 2000 quantization operation (1). To achieve the robustness, we modified (4) to
|π₯(π)|
π¦(π) = sign(π₯(π)) β οΏ½2β οΏ½ οΏ½ + β β π(π)οΏ½, (6)
2β
and (6) β to
V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 367
Image Processing and Earth Remote Sensing
V Fedoseev, T Androsova
π₯(π) + 0.5 β
β β
π ππποΏ½π₯(π)οΏ½ + ππ(π) (π)
π¦(π) = π ππποΏ½π₯(π)οΏ½ β
οΏ½β β
οΏ½ οΏ½ β ππ(π) (π)οΏ½, (7)
β
In the compression process, the values ππ (π’, π£) are used as π₯(π), and βπ are used as the
quantization steps β (see (1)). The obtained quantized values π¦(π) would be the values ππ (π’, π£).
The dependence of the embedding rule on the quantization step β makes it possible to provide
semi-fragility of the embedded information: it will be preserved under compression with quantization
steps smaller than β and lost for steps greater than β.
4. Experiments
To verify the developed watermarking techniques, we embedded a watermark in the Lenna image.
Figure 2 shows the original image on the left and images with hidden information in the center and on
the right. Visual distortions caused by embedding are not noticeable.
Figure 2. Original image (left), and watermarking results: by the modified Simple-QIM (center,
PSNR=64.94) and by the modified DM-QIM (right, PSNR=68.69); quantization parameters
π = 8.5, ππ = 9.
Next, we should make sure that the watermark has the property of semi-fragility. Let π be the
embedded information and π π
be the extracted information. Then the extraction accuracy will be
calculated by the formula:
1 πΎβ1
π = 1 β π΅πΈπ
= 1 β οΏ½ πππ
(π(π), π π
(π)), (8)
πΎ π=0
where π΅πΈπ
is Bit Error Rate.
1
Simple-QIM
0,9
DM-QIM
0,8
Ο
0,7
0,6
0,5
0,4
8,00 9,00 10,00 11,00 12,00 13,00 14,00 15,00 Ξ΅ 16,00
Figure 3. Dependence of the extraction accuracy from ππ (the embedding parameters are π = 8.5,
ππ = 12).
In the second experiment, we compressed images with embedded information (similar to one
shown in Figure 2) using JPEG 2000 standard with different quantization steps βπ determined by ππ
values according to the formulas (2)-(3) (at the fixed π = 8.5). After compression, we attempted to
V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 368
Image Processing and Earth Remote Sensing
V Fedoseev, T Androsova
extract information and to estimate the accuracy using expression (8). The results illustrated in Figure
3 show that the hidden data is preserved at a smaller quantization step (corresponding to a larger value
of ππ than that used in compression). Thus, both algorithms have shown their efficiency in terms of
providing semi-fragility to JPEG 2000 lossy compression. But if we compare two algorithms, we may
conclude that the Simple-QIM graph jump is sharper, i.e., it is closer to the ideal shape. Therefore, the
Simple-QIM modification is more accurate than DM-QIM at the acceptable quantization step border.
Next, we investigated the distortions introduced by information hiding. For this purpose, we used
PSNR and PSNR-HVS metrics. The second one measures image quality from its perception by the
person [10]. Figures 4-5 show the results of this experiment for the image βLennaβ at various ππ (π is
fixed and equal to 8.5). The results confirm that the image does not undergo significant degradation,
and also that image quality is directly related to ππ (the dependence is approximately linear). Thus, the
achieved semi-fragility by ππ can be expressed as semi-fragility by the specified level of PSNR or
PSNR-HVS.
100
80
PSNR
60 Simple-QIM
40 DM-QIM
20
0
8,00 9,00 10,00 11,00 12,00 13,00 14,00 15,00 16,00
ππ
Figure 4. Dependence of PSNR of images with embedded information on the parameter ππ that
determines the quantization step when embedding a watermark (π = 8.5).
120
110
100
90
PSNR-HVS
80
70
60
50 Simple-
40 QIM
30
20
10
0
8,00 9,00 10,00 11,00 12,00 13,00 14,00 15,00 16,00
ππ
Figure 5. Dependence of PSNR-HVS of images with embedded information on the parameter ππ that
determines the quantization step when embedding a watermark (π = 8.5).
5. Conclusion
In this paper, we proposed two watermarking algorithms for semi-fragile data hiding in JPEG 2000
based on QIM concept: Simple-QIM and DM-QIM. Our investigations have shown that both
algorithms provide semi-fragililty property to JPEG 2000: a watermark is preserved under
compression with quality parameters greater than the specified one and is deleted when the
compression quality is reduced. Visually, the distortions caused by embedding the CEH are not
noticeable. Moreover, the measurements of these distortions using PSNR and PSNR-HVS show that
the values are in almost linear dependence on the parameter ππ. It is a very important property which
allows us to control the quality at an acceptable level when information embedding.
V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 369
Image Processing and Earth Remote Sensing
V Fedoseev, T Androsova
6. References
[1] Gonzalez R C and Woods R E 2009 Digital Image Processing (Pearson Education)
[2] Sun Q, Chang S-F, Kurato M and Suto M 2002 A quantitative semi-fragile JPEG2000 image
authentication system Proceedings International Conference on Image Processing 2 IIβII
[3] Maeno K, Sun Q, Chang S-F and Suto M 2006 New semi-fragile image authentication
watermarking techniques using random bias and nonuniform quantization IEEE Transactions
on Multimedia 8 32-45
[4] Preda R O 2013 Semi-fragile watermarking for image authentication with sensitive tamper
localization in the wavelet domain Measurement 46 367-373
[5] Chen B and Wornell G 2001 Quantization Index Modulation: A Class of Provably Good
Methods for Digital Watermarking and Information Embedding IEEE Transaction on
Information Theory 47 1423-1443
[6] Schelkens P, Skodras A and Ebrahimi T 2009 The JPEG 2000 suite (John Wiley & Sons)
[7] Rabbani M and Joshi R 2002 An overview of the JPEG 2000 still image compression standard
Signal Processing: Image Communication 17 3-48
[8] Marcellin M W, Lepley M A, Bilgin A, Flohr T J, Chinen T T and Kasner J H 2002 An
overview of quantization in JPEG 2000 Signal Processing: Image Communication 17 73-84
[9] Mitekin V A and Fedoseev V A 2018 New secure QIM-based information hiding algorithms
Computer Optics 42(1) 118-127 DOI: 10.18287/2412-6179-2018-42-1-118-127
[10] Egiazarian K, Astola J, Ponomarenko N, Lukin V, Battisti F and Carli M 2006 New full-
reference quality metrics based on HVS Proceedings of the Second International Workshop on
Video Processing and Quality Metrics 4
Acknowledgments
This work was supported by the Russian Science Foundation under grant 18-71-00052.
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