1
Bull. Soc. Photogr. Imag. Japan. (2016) Vol. 26 No. 1: 1–9

Original Paper

Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation
Anu Aryal, Shoko Imaizumi, and Takahiko Horiuchi

Abstract:Image scrambling is a method in which an original image turns into a visually degraded image. In this paper, we propose a new algorithm of hierarchical scrambling method for palette-based images using bitwise operation. The target pixels for scrambling were chosen randomly using a pseudo-random number generator and were transformed into their corresponding binary bits. Then, bitwise
operation was applied to concatenate the target pixels and the corresponding random numbers to scramble the palette-based images.
The images in the proposed method are more distorted than those in the conventional methods. We also introduce a hierarchical key
assignment scheme for more secure and efficient communication. We carried out performance tests using standard images. This
demonstrates that our method is superior to the conventional methods.

Key words:

Hierarchical scrambling, Palette-based images, Bitwise operation, Key assignment.

1. Introduction
The recent growth of the Internet has led to the arrival of various
services that require sharing a huge amount of digital media andcontent through their websites. This widespread availability of digital media has created concerns about information leakage and privacy. There are various methods for information security that can beprovided for digital media and communication.
Cryptographic techniques are used to scramble digital content sothat it is extremely difficult for unauthorized users to decode it. Several kinds of scrambling methods are used for image protection. Fullencryption 1), such as advanced encryption standard (AES) and dataencryption standard (DES), is a robust way. However, it does notallow partial viewing and evaluation by users without the proper
keys. Moreover, if there are any errors, it cannot decrypt the content
correctly. Therefore, it is not always the best way to protect privacy
and copyright. Digital watermarking 2) can be used for protecting
copyright and hiding information in digital media such as images,
videos, and audio content. It cannot, however, prevent unauthorized
duplication because it does not visually degrade the quality of theimage. Thus, watermarking also has limitations with regard to privacy protection. On another front, partial-scrambling schemes 3-11),
where a secure algorithm is used to scramble only part of the medium, have been studied. Anyone can view the distorted images thatare distributed by the providers, but only the authorized users candecrypt the images using their proper keys.
Palette-based images are generally limited to 256 colors. Hence,
Received: 18th, December 2015; Accepted: 19th, May 2016

Graduate School of Advanced Integration Science, Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522 Japan

Email: anu@chiba-u.jp, imaizumi@chiba-u.jp, horiuchi@faculty.chiba-u.jp

the amount of data is less than that of full-color images. Therefore,
these images are used in many application areas such as multimedia,
and websites. Furthermore, electronic paper (e-paper) has been very
popular among general consumers lately 12). The commercial successof monochrome e-paper led to the next generation of color e-paper.
Some of the display companies provide not only monochrome e-paper, but also color e-paper. However, there is no full color e-paper inthe current market so far. Moreover, the power consumption is larger as more colors need to be displayed 13).
A flexible partial encryption algorithm for palette-based images
has been proposed 9). However, the pixel scrambling method is notbased on hierarchical access control. Therefore, quality degradation
cannot be controlled depending on the access level granted. On theother hand, one conventional method 10) uses a basic operation thatcan only generate a few patterns. As a consequence, the method
seems unsecure. In a similar way, another conventional method 11)
involves scrambling the palette-based images, that is quite predictable and generates fewer patterns. As a result, these conventional
methods are not secure enough to prevent malicious decryption by
unauthorized users.
We propose a new hierarchical scrambling method for palette-based images using bitwise operation in this paper. In the proposed method, the target pixel threshold and the number of target
color components (red (R), green (G), and blue (B)) are the main
parameters for the quality control of scrambled images. The proposed method can degrade images more than the conventional
methods 10)11) and also provides a secure environment against unau-

2

Bull. Soc. Photogr. Imag. Japan. Vol. 26 No. 1 (2016)

thorized decryption by using hierarchical key assignment schemes 14)15).
The rest of this paper is organized as follows. In section 2, we
present the technical specifications of our method. Then, we elaborate on the related works in section 3. In section 4, we describe theproposed hierarchical scrambling method in detail. The experimental results are demonstrated in section 5. We conclude this paper insection 6.

2. PRELIMINARY
Before discussing the proposed method, we describe some related
technical terms. The colors in the palette are referred to as entities,
and an index number is assigned to each entity. Each pixel in theimage has an index number, and the index number indicates thecorresponding entity, which specifies the RGB color.
2.1 Composition of Color Palette in Palette-Based Images
As shown in Fig. 1, each index number i of a color palette contains information specifying where the entity is located in the palette. The color palette itself is an array consisting of three columns
corresponding to the primary colors R, G, and B, and N rows, where
N is the number of entities in the palette. In the case of N = 256, theimage is an 8-bit image.
2.2 Hierarchical Access Control
Access control is a technique to prevent unauthorized users accessing confidential information. Hierarchical key assignment
schemes 14)15) generate encryption keys so that the keys of a higher
class can be used to derive the keys of all lower classes in the hierarchy. These schemes enable control of the quality level of the target
information to be accessed.
Here, we assume that the scalable medium has two-dimensional
scalability (D = 2). One of the dimensions describes the ratio of the
pixels to be scrambled, and the other is the R, G, and B color components. Figure 2 depicts the hierarchical scrambling of two-dimensional scalability. The expected lowest quality image is R100 G100 B100.
The scrambling process is carried out on each entity with its corresponding key generated by a hierarchical key assignment scheme.

Fig. 2. Hierarchical scrambling of two-dimensional scalability (D = 2)15)

3. RELATED WORK
3.1 Hierarchical Scrambling of Palette-Based Images 10)
One conventional method for hierarchical scrambling of palette-based images 10) cyclically shifts the binary digits to scramble
the images. The principal steps for this method are described as follows.
Step 1. Assume Ei is the i-th entity in the color palette.
Step 2. Calculate the frequency Fi of the i-th entity Ei.
Step 3. Sort all the entities Ei in ascending or descending order
based on their frequencies Fi as shown in Fig. 3.
Step 4. Change the index number i of each entity Ei to j on the basisof the sorted color palette. Therefore, entities Ei are changed
to Ej simultaneously.
Step 5. Select the target entities to be manipulated from the sorted
palette in a hierarchical order.
Step 6. Extract an 8-bit binary value from R, G, or B values.
Step 7. Perform the cyclic shift operation on the binarized RGB
values using a pseudo-random number.
For instance, as shown in Fig. 4, the decimal value ‘76’ is chosen to
be manipulated and is converted into the binary value equivalent of(01001100)2. In the next step, a random number is chosen in therange 0 to 7 by using a pseudo-random number generator with its
defined seed. In Fig. 4, the rightmost two digits are then cyclicallyshifted to the left, and the binary value becomes (00010011)2, whichis equivalent to ‘19’. In other words, this method performs cyclic
shifts to manipulate the binary bits and scramble the images.
The major drawbacks of this approach are: 1) the cyclic shift operation is simple to perform and 2) we can change the current color
in the palette to another color only from a limited range of colors
due to the use of cyclic shift. Therefore, this method is vulnerable tomalicious attacks, such as brute force attacks 16). Our main objectivein this paper is proposing a more secure method.

Fig. 1. Structure of palette-based image

3.2 Hierarchical Scrambling of Palette-Based Images Using Transposition Cipher 11)
The basic idea of the conventional method 11) is based on changing the positions of the R, G, and B. In Step 6 of 3.1, the positions

Anu Aryal, Shoko Imaizumi, and Takahiko Horiuchi

Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation

3

4.1 Parameters for quality control
To control the quality of the scrambled images, we introduce twoparameters: target pixels and target color components.
4.1.1 Target pixels: Figure 1 shows an example of the relation between the index number of entities and their frequency of use. Figure 3 shows a color palette sorted in ascending order and the processof selecting the frequencies of pixels in a hierarchical manner. The
proposed method can control the quality of scrambled images andselect scrambling from high-frequency regions when the color palette is sorted in ascending order or from low-frequency regions
when the color palette is sorted in descending order.
Fig. 3. Histogram sorted in ascending orderFig. 4. Cyclic shift in conventional method 10)of the RGB values of the target entities are randomly shuffled asshown in Fig. 5 to form the scrambled image. However, this scrambling process is too simple, and we can generate only a small numberof patterns. Therefore, it is difficult to achieve sufficient complexity
in the decryption process to thwart unauthorized users.

4. PROPOSED METHOD
In this section, we propose a hierarchical scrambling method for
palette-based images using bitwise operation. The proposed method
is superior to the conventional methods 10) 11) in terms of the distortion of the scrambled images and security against malicious users.

Fig. 5. Procedure of conventional method 11)

4.1.2 Target color components: The process of manipulating thetarget RGB values is shown in Fig. 2. For instance, 10% of the
R-component is first scrambled. The scrambled component is denoted as the R10 component. The scrambled image is then passed tothe operation on the right to scramble 10% of the G-component, so
the image is scrambled with the R10 and G10 components. In the next
step, the result is again passed to the operation on the right for 10%
scrambling of the B-component to obtain the R10, G10, and B10 components. In the case of 20% scrambling, the R10 component is passed
to the operation below for the next 10% scrambling to obtain the R20
scrambled image. The resultant R20 is then passed to the operationon the right, and the G10 component is passed to the operation below
for the next 10% scrambling, so the image is scrambled with the R20
and G20 components. In the next step, the resultant R20 and G20 are
passed to the operation on the right, and B10 is passed to the operation below for the next 10% scrambling to obtain the scrambled
image with R20, G20, and B20 components. This process is repeated forall pixels.
4.2 Algorithm
Fig. 6 shows the outline of the proposed method. We describe thescrambling procedure of our method in detail.
Step 1. Assume Ei is the i-th entity in the color palette.
Step 2. Calculate the frequency Fi of the i-th entity Ei.
Step 3. Sort all the entities Ei in ascending or descending order
based on their frequencies Fi as shown in Fig. 3.
Step 4. Change the index number i of each entity Ei to j on the basisof the sorted color palette. Therefore, entities Ei are changed
to Ej simultaneously.
Step 5. Select the target entities to be manipulated from the sorted
palette in a hierarchical order.
Step 6. Extract an 8-bit binary value from R, G, or B values.
Step 7. Choose a random number and perform modulo operation
with respect to N, where N is 256. The decimal result is thenconverted into its binary value as shown in Fig. 6.
Step 8. Apply bitwise operation to the binary results from Steps 6
and 7.
Step 9. Repeat Steps 5-8 until all of the target entities and the target
components are modified.
Steps 1 to 6 are carried out as described above in section 3.1 for this4

Bull. Soc. Photogr. Imag. Japan. Vol. 26 No. 1 (2016)

10%

Key B

Key G

Key R

Scrambling

KR10

H

KG10

KB10

H

H

H

H

KR20

KG20

KB20

H

H

H

30%

KR30

KG30

KB30

100%

KR100

KG100

KB100

20%

Fig. 7. Key assignment using SHCs and RHCs
Fig. 6. Overview of proposed method

method. Note that we adopt XOR operation as the bitwise operation in this paper.
4.3 Key assignment and Encryption
We have adopted hierarchical key assignment schemes using simple hash chains (SHCs) and recursive hash chains (RHCs)15), as depicted in Fig. 7. Basically, each scrambling class is assigned a key thatcan generate the keys of all lower classes in the hierarchy.
Access control for scalable media should encrypt the codestream
entity-by-entity by using different keys. Keys for encryption are derived as shown in Fig. 7. For example, keys KR10, KG10, and KB10 are
the keys for the R10, G10, and B10 components, respectively. KR10 is
the managed key. KR20 is derived from KR10 as follows:
KR20 = H (KR10),(1)where H ( · ) is a cryptographic one-way hash function, i.e., SHA256 17). Similarly, the other keys KRs (s = 30, 40, …, 100) are derived
with SHCs.

make the security robust against collusion attacks. These keys are theseeds of pseudo-random numbers in this context. Moreover, the key
management of keeping only a single key KR10 is quite simple. Notethat the decryption procedure requires both the pseudo-random
numbers and the keys.
4.4 Key delivery and decryption
As shown in Fig. 8, if the user is allowed to access the image with
the quality of R20G20B20, the user receives the three keys KR30, KG30,
and KB30. The 21 keys KR40, KG40, …, KB100 are derived from the threedelivered keys by using SHCs. The user can decrypt the corresponding 24 components such as the R30, G30, …, B100 components.
Similarly, as shown in Fig. 9, if the user is allowed to access theimage with the quality of R100G100B0, the user receives key KB10. The
9 keys are derived from the delivered key using SHCs. The user can
decrypt the corresponding 10 components such as B10, B20, …,
B100 components.
Scrambling
10%

KG10 = H (f (KR10, H (KR10)))(2)= H (f (KR10, KR20)),
and

Key G

KR10

KG10

H

Key B
H

KB10

H

H

H

KR20

KG20

KB20

H

H

H

30%

KR30

KG30

KB30

100%

KR100

KG100

KB100

20%

On the other hand, keys KG10 and KB10 are derived by RHCs as
follows:

Key R

Delivered keys to user

Fig. 8. Decryption process for image quality of R20G20B20

KB10 = H (f (KG10, H (KG10)))(3)= H (f (KG10, KG20)),

where f ( · ) is a function that represents bitwise XOR operation
between the two inputs.
In this case, multiple keys can be recursively computed from themaster key by using hash chains. The main benefits of using hashchains are: 1) they can reduce the complication of key managementand delivery and 2) they can be independent from each other, which

Scrambling
10%

KR10

Key B

Key G

Key R
H

KG10

H

KB10

H

H

H

20%

KR20

KG20

KB20

H

H

H

30%

KR30

KG30

KB30

100%

KR100

KG100

KB100

Delivered key to user

Fig. 9. Decryption process for image quality of R100G100B0

Anu Aryal, Shoko Imaizumi, and Takahiko Horiuchi

Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation

5. EXPERIMENTAL RESULTS
The proposed and conventional methods 10)11) were examined byusing ten different images available from the Kodak lossless true
color image suite 18) as shown in Fig. 10. The original images are oftwo different sizes: 512 × 768 and 768 ×512. The private text included in the images, such as the number plate of an automobile, is an
important factor to consider for security. Therefore, we utilized someof the original images such as kodim03, kodim08, kodim09, kodim10, and kodim14 to check if the text in those images can be recognized after scrambling. Similarly, as it is important to evaluate thedominant ratio of human faces in the image, we considered images
with small (kodim12 and 14), medium (kodim 18), and large faces(kodim04 and 15) as shown in Fig. 10 for the simulation.
For the simulation, the colors of the 24-bit images were reduced
before scrambling. The minimum variance quantization was performed on the original images by calling matlab function ‘rgb2ind’,specifying the maximum number of colors in the output as 32 (5bit), 64 (6-bit), 128 (7-bit), and 256 (8-bit) colors without setting
dithering option. We carried out a performance evaluation with thestructural similarity (SSIM) index 19), peak signal-to-noise ratio(PSNR), and mean square error (MSE) between the original image
and the visually distorted images for the proposed method as well as
the conventional methods. The parameters used while calculating(a) kodim03(d) kodim12(g) kodim04(b) kodim05(e) kodim14(h) kodim09(c) kodim08(f) kodim15(i) kodim10(j) kodim18

Fig. 10. Original images from Kodak lossless true color image suite 18)

5

the SSIM values were defined as K (constants in the SSIM index
formula) = [0.01, 0.03], Window (local window value) = 11, and L(dynamic range of image) = 1.
5.1 Analysis of Image quality
Figure 11 shows 30%, 60%, and 100% scrambling of the original
image ‘kodim09’ with the maximum number of colors as 256. The
quality of the scrambled images is gradually distorted with increasing percentage of target pixels. Furthermore, note that the text included in the original image ‘kodim09’ is also gradually distorted
and is difficult to read as the percentage of scrambling is increased.
Figure 12 shows the three scrambled images of ‘kodim09’ with 32
colors, where the quality is R100G100B100, using different seeds for thepseudo-random generator. Figure 12 (a), (b), and (c) look different
from each other. For example, the text in the image in Fig. 12 (a) has
been distorted the most as we cannot recognize the text. On theother hand, Fig. 12 (c) has been distorted less as we can easily readthe text. Therefore, the quality of the scrambled images can be controlled by using different seeds. Figure 13 shows the monochrome
version of the scrambled images of image ‘kodim09’ with the intensity level as 32. The proposed method is applied on ‘image09’ afterconverting to monochrome version. It is seen that the quality of thescrambled image is degraded hierarchically for the monochromeimage also. Figure 14 shows the full scrambled images of ‘kodim09’(monochrome) with 32 intensity levels, using three different seeds.
As shown in the simulations, it is difficult to see whether people arepresent in the ships or not. In addition, the text written on the flagof the ship, which is present at the back, are distorted completely andare invisible. On the other hand, the upper text written on the flag ofship, which is present at the front is visible, whereas the lower text is
considerably degraded and is difficult to read the numbers.
Figure 15 shows 30%, 60%, and 100% scrambling of the original
image ‘kodim18’ with the maximum number of colors as 256. In thecase of 100%, the image is distorted the most. Note that the color of
the woman’s skin and the background, where she is standing slowly
become distorted with increasing percentage of target pixels. At thequality of R100G100B100 scrambling, it is hard to see the actual color ofher skin and background of image. From the simulation results, we
can also observe that the visual quality is degraded as the number oftarget color components is increased. In general, the simulation results show that our hierarchical scrambling scheme is effective forthese test configurations. In other words, the image quality is degraded in a hierarchical manner as the percentage of target pixels isincreased. Figure 16 shows the three scrambled images of ‘kodim18’
with 32 colors, where the quality is R100G100B100, using different
seeds. Figure 16 (a), (b), and (c) are different. It is difficult to see theactual color of woman’s necklace in all of the figures, but the outlineof her necklace is visible and cannot be hidden. Figure 17 represents
the monochrome version of the scrambled images of original image‘kodim18’ with 32 intensity levels. Figure 18 shows the full scrambled images of ‘kodim18’ (monochrome) with 32 intensity levels,
using three different seeds. As shown in the simulation results, theface of the woman is distorted and is difficult to see.

6Bull. Soc. Photogr. Imag. Japan. Vol. 26 No. 1 (2016)(a) R30G0B0(b) R30G30B0(c) R30G30B30(d) R60G0B0(e) R60G60B0(f) R60G60B60(g) R100G0B0(h) R100G100B0(i) R100G100B100(a) 10%(b) 20%(c) 30%(d) 40%(e) 50%(f) 60%(g) 70%(h) 80%(i) 90%Fig. 11. Scrambled images in proposed method (‘kodim09’ with 256 colors)(j) 100%

Fig. 13. Scrambled images in proposed method (‘kodim09’ with 32 intensity
levels, monochrome)(a) Seeding pattern A(b) Seeding pattern B(c) Seeding pattern C

Fig. 12. Full scrambled images (R100G100B100) of ‘kodim09’ with 32 colors inproposed method using three different seeds

5.2 Analysis of SSIM, PSNR, and MSE metricWe evaluated the quantitative performance of the proposed
scheme by calculating the SSIM, PSNR, and MSE values of all the
distorted images. Figure 19 shows the SSIM results of ‘kodim18’
that were obtained by scrambling the R, G, and B components where
the maximum numbers of colors were 32, 64, 128, and 256. We controlled two parameters: the target pixels and the target color components in the experiment. The target pixels were chosen from thetarget color components in a hierarchical manner. We realize that
there is a hierarchical scrambling of images for all quantized colors.
Figure 20 and 21 show the PSNR and MSE values of ‘kodim18’
obtained by scrambling the R, G, and B components for different
numbers of colors. We can observe that the value of PSNR decreases as the percentage of scrambling increases. The value of MSE increases as the percentage of scrambling increases because the higher(a) Seeding pattern A(b) Seeding pattern B(c) Seeding pattern C

Fig. 14. Full scrambled images of ‘kodim09’ (monochrome) with 32 intensity
levels in proposed method using three different seeds

the value of MSE, the higher the distortion level is. Figure 22, 23,
and 24 show the results of SSIM, PSNR, and MSE, respectively, of‘kodim18’ with 256 colors by scrambling (R), (R and G), and (R, G,
and B) components. These graphs show that the scrambling process
occurred in a hierarchical manner.
Table 1 shows the SSIM, PSNR, and MSE values for ‘kodim18’

Anu Aryal, Shoko Imaizumi, and Takahiko Horiuchi

7

Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation(a) R30G0B0(b) R30G30B0(c) R30G30B30(a) 10%(b) 20%(c) 30%(d) R60G0B0(e) R60G60B0(f) R60G60B60(d) 40%(e) 50%(f) 60%(g) R100G0B0(h) R100G100B0(i) R100G100B100(g) 70%(h) 80%(i) 90%Fig. 15. Scrambled images in proposed method (‘kodim18’ with 256 colors)(j) 100%

Fig. 17. Scrambled images in proposed method (‘kodim18’ with 32 intensity
levels, monochrome)(a) Seeding pattern A(b) Seeding pattern B(c) Seeding pattern C

Fig. 16. Full scrambled images (R100G100B100) of ‘kodim18’ with 32 colors inproposed method using three different seeds

with 256 colors where the target color components are the R, G, andB components. The values of SSIM in the proposed method are lower compared with those of the two conventional methods 10)11). The
proposed method can distort the images more than the two conventional methods. Furthermore, the proposed method has the mostdegraded image because the value of SSIM for 100% scrambling in
the proposed method is much lower, i.e. 0.006, than that of othertwo methods, which have SSIM values of 0.082 and 0.311. Similarly, the conventional methods have larger values of PSNR than the
proposed method. The MSE values in the proposed method are
higher than those of the conventional methods. Therefore, the images are more distorted in the proposed method than in the conventional methods 10)11).
The proposed method is quite efficient compared to theabove-mentioned methods 10)11) because in the proposed method we(a) Seeding pattern A(b) Seeding pattern B(c) Seeding pattern C

Fig. 18. Full scrambled images of ‘kodim18’ (monochrome) with 32 intensity
levels in proposed method using three different seeds

can change the current color in the palette to another one from the24-bit colors, that is, full colors. The conventional method 10) has acyclic shift operation to manipulate the binary bits by choosing arandom number in the range 0 to 7. Therefore, we can change thecurrent color in the palette to another one only from the limited
colors but not from the full colors. Similarly, the conventional method 11) shuffled the positions of the RGB values of target entities ran-

8Bull. Soc. Photogr. Imag. Japan. Vol. 26 No. 1 (2016)

1
0.9
0.8
0.7
32

SSIM

0.6

64

0.5

128

0.4

256

0.3
0.2
0.1
0

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Percentage of scrambled pixels (%)

Fig. 19. SSIM values of ‘kodim18’ obtained by scrambling R, G, and B
components for different numbers of colors

Fig. 20. PSNR values of ‘kodim18’ obtained by scrambling R, G, and B
components for different numbers of colors

Fig. 21. MSE values of ‘kodim18’ obtained by scrambling R, G, and B components for different numbers of colors

domly in the range 1 to 256. Hence, we cannot change the colors to
one from the full colors.

6. CONCLUSION
In this paper, we have proposed a new algorithm for a hierarchi-

Fig. 22. SSIM values of ‘kodim18’ with 256 colors obtained by scrambling(R), (R and G), and (R, G, and B) components

Fig. 23. PSNR values of ‘kodim18’ with 256 colors obtained by scrambling(R), (R and G), and (R, G, and B) components

Fig. 24. MSE values of ‘kodim18’ with 256 colors obtained by scrambling(R), (R and G), and (R, G, and B) components

cal scrambling method for palette-based images using bitwise operation. The proposed method distorts the images more than the conventional methods 10)11) and also enhances the security of scramblingfor palette-based images. The colors in an image can be changed to
any color, from the 24-bit colors in our proposed method, whereas

Anu Aryal, Shoko Imaizumi, and Takahiko Horiuchi

Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation

9

Table 1. SSIM, PSNR, and MSE values of ‘kodim18’ with 256 colors obtained by scrambling R, G, and B components
Percentage ofscrambled pixels (%)
10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SSIM

Prop.

Conv. 10) Conv. 11)

0.635

0.683

0.844

0.436

0.327

0.239

0.139

0.082

0.049

0.010

0.006

0.915

0.537

0.386

0.318

0.204

0.160

0.126

0.085

0.082

0.861

0.749

0.697

0.573

0.509

0.475

0.413

0.354

0.313

0.311

there is a restriction on changing colors from the full colors in theconventional methods. Therefore, the proposed algorithm is moresecure against unauthorized decryption compared to the conventional methods. Moreover, it can control the quality of scrambled
images using only a single managed key. Our future work involves
consideration of additional parameters to develop a more robust
platform for effective sharing and controlling of the target images.

Acknowledgement
This work was supported by Venture Business Laboratory (VBL)
Research Project of Chiba University.

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PSNR

Prop.

Conv. 10) Conv. 11)

20.08820.97823.192

17.26715.962

14.697

13.57312.14911.58810.76910.40726.07219.36016.99915.91314.50413.612

13.16912.46512.22024.65822.65921.98520.30319.85519.72119.332

19.12318.91418.880MSE

Prop.

Conv. 10) Conv. 11)

0.010

0.008

0.005

0.019

0.025

0.034

0.044

0.061

0.069

0.084

0.091

0.002

0.012

0.020

0.026

0.035

0.044

0.048

0.057

0.060

0.003

0.005

0.006

0.009

0.010

0.011

0.012

0.012

0.013

0.013

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