Robust Index Code with Digital Images on the Internet

Minsu Kim

, Kunwoo Lee

, Katsuhiko Gondow and Jun-ichi Imura

Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan

Keywords:

Bar Codes, Robustness, Digital Images, Image Databases, Image Color Analysis, Encoding.

Abstract:

A new color code which has high robustness is proposed, called Robust Index Code (RIC for short). RIC can

be used on digital images and link them with the database. There are several technologies embedding data into

images such as QR Code and Digital watermark. QR Code cannot be used on digital images because it does

not have robustness on digital images. Besides Digital watermark can be used on digital images, but embedded

data cannot be extracted 100% on damaged images. From evaluation using our implemented RIC encoder and

decoder, the encoded indexes can be extracted 100% on compressed images to 30%. We also implemented a

doubt color correction algorithm for damaged images. In conclusion, RIC has the high robustness on digital

images. Hence, it is able to store all the type of digital products by embedding indexes into digital images

to access database, which means it makes a Superdistribution system with digital images realized. Therefore

RIC has the potential for new Internet image services, since all the images encoded by RIC are possible to

access original products anywhere.

1 INTRODUCTION

With easily being able to retrieve digital contents on

cell phones, Internet services which focus on images

have been growing. Nowadays, over 350 million

images are uploaded to Facebook every day (inter-

net.org, 2013). In spite that the trend of mobile con-

tents continues to increase, the complete recoverable

data embedding method into digital images on the In-

ternet is not realized.

The techniques which can store data into printed

images were developed such as barcode, QR Code

and CQR Code (Nurwono and Kosala, 2009). How-

ever, these technologies have several problems for us-

ing on digital images. QR Code can only store data

up to 23,648 bits, which is very small in contrast to

digital contents and image size (Incorporated., 2008).

CQR Code is a color code which can store 9KB data

(Nurwono and Kosala, 2009). Still, 9KB data limit is

too small to store digital multiform contents.

Another problem is damage of data by random

degradation on the Internet. When images are up-

loaded typically on Facebook, they are resized or

compressed for the fast response. On image degrada-

tion, a stored data by QR Code get damaged so that it

cannot be extracted correctly. For this reason, digital

watermarking, a method to covertly embed some in-

Corresponding author of this paper.

formation in multimedia objects, has received signif-

icant attention. In spite that watermarking has many

different applications, watermarking schemes do not

work effectivelyfor all types of media and universally

for various diverse applications (Mintzer et al., 1997).

Its robustness is very high compared to QR Code, but

it is still not guaranteed that embedded images can be

100% extracted by digital watermarks (Mohanty and

Bhargava, 2008).

This paper proposes Robust Index Code called

RIC, which is a brand new color code that has high

robustness and can link with unlimited data. RIC

guarantees to restore 100% original embedded data in

the deﬁned speciﬁcation, which means an index of N-

bits to access to a remote database can also be safely

embedded into a RIC image so that we can link with

multiformed and unlimited data in the remote server.

We implemented RIC and evaluated its performance

and robustness through extensive experiments under

a range of settings on speciﬁc parameters. The result

shows that the data of RIC are extracted 100% on the

images over 420 pixels Resize and 30% JPEG Com-

pression.

From the result, according to RIC concept shown

in Figure 1, users can embed text, link, music, video,

VR contents into a digital image with high robust-

ness by RIC. As shown in Figure 1, only a database

index encoded with RIC is actually embedded in im-

Kim, M., Lee, K., Gondow, K. and Imura, J-i.

Robust Index Code with Digital Images on the Internet.

DOI: 10.5220/0005951400280037

In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 5: SIGMAP, pages 28-37

ISBN: 978-989-758-196-0

Remote Database

Image

RIC

<Database

Index>

Text Link Music Video VR

Figure 1: RIC Concept.

ages. Moreover, we can expect a new Superdistribu-

tion system based on RIC which is not restricted by

any existing or new Internet platforms. Because the

RIC image is platform-independent, anyone can copy

and share it in all the ways, such as E-mail, SNS or

mobile messenger. In addition, the RIC library sup-

porting multiple platforms will be offered for free.

Hence, it is possible that any users instinctively ac-

cess to the embedded data in the RIC images very eas-

ily without restricting platforms. It is highly expected

that the Superdistribution system by RIC be one of

the fastest and strongest distribution routes of digital

products and communication, regardless of platforms

on the Internet.

Some expected examples are cited as follows. For

instance, many talented artists may embed their con-

tents and information of themselves, and retail shops

may embed the links for their e-commerce system or

promotion information into the their attractive images

to collect trafﬁc on the Internet. The media is also eas-

ily expected to embed the recent issues in the intrigu-

ing pictures to attract people. In case of software com-

panies, the relatively big size contents such as games,

VR contents, and so on are expected. Subsequently,

those digital products embedded in the RIC images

spread out by the honest needs and judgment of typi-

cal users to personally consume them on the Internet,

not on the existing platforms. It means the quality

and the prices of the products are only the consider-

ations of selection and purchase without any existing

interference and it is a relatively desirable system for

both owners and recipients. Ultimately, this proposal

aims to improve the rights of product owners and con-

sumers, by the renovation of a distribution process

and proﬁt structure on the Internet.

2 RELATED WORK

There are more than 20 types of conventional 2D

codes: QR Code (Incorporated., 2008), Color Quick

Reference Code (Nurwono and Kosala, 2009), Mi-

crosoft’s High Capacity Color Barcode (HCCB) (Re-

search, 2010), etc. The papers (Grillo et al., 2010;

Hao et al., 2012; Liu et al., 2008; Yeh and Chen,

1998) provide examples of 2D codes. The main dif-

ference among the presented codes is the limited data

size. Because they are targeting on mainly off-line

printed images, the capacity of data is one of the main

challenges of them (Grillo et al., 2010). In Addition,

the ﬁnder pattern boxes on existing Codemark can be

easily damaged by Resize. Therefore, in spite of high

recognitionrate on printed images, the data embedded

by the presented codes get damaged on image degra-

dation such as Resize, JPEG Compression. They are

not suitable for digital images on the Internet.

Digital watermark is another data embedding

method on images which has high robustness on im-

age degradation. The data embedded by watermark-

ing algorithms are not damaged by JPEG Compres-

sion, Gaussian noise, Cropping and Resize (Woo

et al., 2005; Jiansheng et al., 2009; Wang et al., 2007).

But due to the purpose of digital watermark which is

ownership evidence or ﬁngerprinting, the watermark

detection does not succeed 100% (Woo et al., 2005;

Jiansheng et al., 2009), which means that everyone

who downloads an embedded image cannot see the

same data. The paper (Jiang and Armstrong, 2002)

tries to embed indexes into images using digital wa-

termark. Despite its large encoded size, data loss oc-

curs by JPEG Compression. Steganography similar

to Digital watermark has high robustness too, but also

embedded data can not be detected fully (Liu et al.,

2011; Provos and Honeyman, 2001).

3 ROBUST INDEX CODE

Robust Index Code, named RIC in this paper, is a new

color index code which has high robustness. The idea

is to develop a new color code which embeds not real

digital products but a database index. Hence, RIC

should not be damaged by sharing Internet services

such as E-mail, Messanger, SNS. When users obtain

an image from the Internet service, there is high pos-

sibility that the image got damaged by Resize and

JPEG Compression for the fast response on the ser-

vice. So RIC focuses on Resize and JPEG Compres-

sion. In addition, to link with unlimited data, RIC

embeds only 50-bit index into each digital image, as-

suming a server on the Internet to store its content

Robust Index Code with Digital Images on the Internet

data. About 350 million images are uploaded to Face-

book every day (internet.org, 2013). So 50-bit index

is reasonably enough for index size.

Our concept is to design a new color code which

has high robustness so that we can embed an index

key of a remote database which stores lots of data.

In addition, an encoded image should be able to be

decoded on every device which can access on the In-

ternet such as a mobile phone and PC browser. For

this reason, we developed a C++ encoder / decoder

prototype library to be deployed on iOS, Android

and Chrome Extension. By using a mobile phone or

Chrome browser on PC, users can easily and directly

see the contents of an encoded image on the Internet.

There is an example of the RIC library, which is ex-

plained in Appendix.

RIC consists of encoding and decoding parts. This

section describes encoding part with the design of

RIC and decoding part with a new algorithm.

3.1 Robust Index Code Design

Our goal is to design a color code which has robust-

ness on the most common image degradation on the

Internet like Resize and JPEG Compression. The rea-

son why QR Code gets damaged by Resize or JPEG

Compression is that data pixels are infected by other

pixels. These are due to the quantization step of the

JPEG Compression (Furht, 1995). To solve those

problems, a data pixel should be larger than other

codes and the distance between data pixels should be

long enough not to be infected by neighbor pixels. So

we propose a circle of a ﬁxed size, which radius is r

pixel, for inserting bit data and the distance between

two circles is longer than a constant d pixel which is

shown in Figure 2. The idea of embedded spot is simi-

lar to CQR Code (Nurwono and Kosala, 2009), which

is 24-bit RGB color. On each circle, 3 bits data can be

embedded by 1 bit to R, 1 bit to G and 1 bit to B with

converting 0 to color D

and 1 to color D

. Figure 2 is

an example of setting D

= 80 and D

= 255.

r r

R= 80 → 0

G= 80 → 0

B=255 → 1

Figure 2: Example of RIC circle.

In order to detect an encoded area on an image,

RIC includes a square wrapping data circles. The

square’s background color is light gray for not infect-

ing data circles. For more accurate detection, the bor-

der of square is added with light black color like (Ong

et al., 2008). Assuming r to 4, d to 2, D

to 80 and

to 255, the ﬁnal design is shown in Figure 3 used

in encoding, decoding and evaluating.

Figure 3: Example pattern of RIC.

From our design, the capacity of RIC is shown in

the following formula:

capacity(bit) = 3N

s.t. 0 < N

≤ (

s+ d− 2p

2r+ d

)

(1)

where s is a side pixel of the square without the bor-

der, p is padding pixels of the square (assumed to 6)

and N

is the number of circles. Figure 3 contains 22

circles, for the design reason to represent P, so that 66

bits can be embedded. It is enough capacity to embed

database index, which size is 50 bits.

To determine RIC is correctly encoded, RIC has

a checksum algorithm. On RIC, a checksum is in-

serted on c bits and an index is inserted on the rest of

bits. The algorithm is simple that a checksum value is

the remainder when an index is divided into 2

. That

means we can determine true RIC with 1/2

proba-

bility. In this paper, we decide on the bit length of

checksum as 16.

3.2 Robust Index Code Decoder

Decoding RIC consists of three main parts, determin-

ing the data square, extracting bit data on data circles

with damage check, validating extracted data. The

progress of decoding is shown in Figure 4.

Decoding

Square

Exist?

Not Encoded

Image

Damaged

Yes

Checksum

Validate?

Yes

Success

Attacked Image or

Not Encoded Image

Too Damaged

Image

DB Index

Check?

Yes

Not Original

Image

Extract data

of one circle

Doubt Color

Correction

Extract all

circles?

Yes

Figure 4: Decoding process.

• Step 1: A data square can be determined with

High-pass ﬁlter and Low-pass ﬁlter. Because the

border of a square could be uneven by image

degradation, a square itself is used for detecting.

Still, when the square cannot be extracted, the im-

age is regarded as not RIC image or extremely

SIGMAP 2016 - International Conference on Signal Processing and Multimedia Applications

indexList := new List();

indexList.add(new int[circles.size()*3]);

For

=0 To circles.size()-1 Dd

: 0

→

R, 1

→

G, 2

→

For

=0 To 2 Do

// Check

Color damaged

bool damaged :=

isColorDamaged(circles[

);

For

=0 To indexList.size()-1 Do

int *original := indexList[

];

If damaged =

true

Then

// add both possibilities to the list

int *cloned := original.clone();

original[

*3+

] := 0;

cloned[

*3+

] := 1;

indexList.add(cloned);

Else

// add extracted bit to the list

original[

*3+

] :=

extractBinary(circles[

);

End If

End For

If indexList.size()

> N d

Then

Print "Too Damaged Image";

End If

End For

Figure 5: Doubt Color Correction Algorithm.

damaged image which means out of speciﬁcation

for the performance cost.

• Step 2: Bit data is extracted from the center of

data circles. Although D

or D

color circles are

drawn on Encoding process, there is a high pos-

sibility that the color is not D

or D

by image

degradation. Therefore we propose a new algo-

rithm for color damage checking, doubt color cor-

rection algorithm in Figure 5.

When an extracted color from a data circle is far

from D

and D

, we cannot determine the origi-

nal bit data was 0 or 1. Hence, the algorithm adds

both 0 and 1 for that doubtable color into possible

index list, called doubt list in this paper,so that ac-

tual original index is always included in the doubt

list. In addition, the algorithm puts on the limit of

the size of the doubt list, N

. If the size oversteps

the limit, the decoder recognizes the image as a

too damaged image and stop decoding. With this

algorithm, the decoder can recover damaged RIC.

• Step 3: From the second step, the decoder extracts

multiple indexes, which is the doubt list. To ex-

tract exactly the original index, RIC decoder need

to validate the doubt list. In this step, RIC de-

coder checks two algorithms. One is validating

checksum value of extracted data. The other is to

check image’s inclinations matching up with orig-

inal image’s attributes of the database with the ex-

tracted index, such as Brightness, Histogram, Ra-

tio, Keypoints based on several image processing

theories. At last, a few validated-extracted data

remains. We expect the size of the last remained

doubt list is only one. If it’s more than one, the

user picks the data.

4 EVALUATION

The evaluation of the RIC algorithm is involved with

robustness. We used a Peak Signal-to-Noise Ratio

(PSNR) as a comparison parameter between a dam-

aged image and an original image. The bigger value

of PSNR implies the less damaged image and the in-

ﬁnity value of PSNR implies the original image (Jian-

sheng et al., 2009; Hore and Ziou, 2010). We exper-

imented the prototype of RIC encoder/decoder with

Figure 3 pattern, developed by C++ using OpenCV (It-

seez, 2000). The experiments are simulated by watch-

ing the success number of decoding attacked images

with assuming r to 4, D

to 80, D

to 255, p to 6,

to 22, N

to 2

. The process consists of encod-

ing 10,000 pictures, attacking the images and decod-

ing the attacked encoded images. We used 9,850 pri-

vate images, which mainly pictured with landscapes,

foods and people, and 150 downloaded images from

free photo archivesmorgueFile(Connors et al., 1996),

which width is 1280 px. Also, to check DB index

in this paper, on encoding step, the original images’

attributes are saved on the memory. So on decoding

step, decoder checked the extracted index and image’s

ratio matching up the data saved above. In this ex-

periment, we exclude detecting and matching image’s

inclination.

4.1 Distance of Circles

We experimented on ﬁnding proper d (the distance of

circles). The distance evaluation was done by assum-

ing d to 0, 1, 2, 3, 7, 9 pixels. Also for measuring how

much the distance is related with damage, validating

DB index and image’s ratios process are not included

on this experiment. To evaluate robustness, before de-

coding images, we attack the encoded images by 30%

JPEG Compression. The result is shown in Table 1.

The distance of circles is related highly with the

decoding performance. Table 1 shows that the longer

distance of circles makes the size of doubt list con-

verge 1, which means RIC is not easily damaged.

While long distance makes the accuracy of decoding

RIC high, we want to make RIC area smaller with op-

timized performance. Also more image attacks could

widen the size of doubt list difference. For this rea-

Robust Index Code with Digital Images on the Internet

Table 1: Distance Experiment Result.

Distance

Success

Rates

Size Avg of

Doubt List

Decode

Time Avg

0 1.0000 1.1613 0.0497s

1 1.0000 1.1267 0.0531s

2 1.0000 1.0555 0.0590s

3 1.0000 1.0228 0.0569s

7 1.0000 1.0000 0.0714s

9 1.0000 1.0045 0.0789s

son, we choose to set d to 2 in this paper among the

shortest distance for the size of doubt list to be smaller

than 1.1.

4.2 Robustness

For evaluating RIC’s robustness, the experiment in-

cludes various attack types. Robustness experiment

has been held with experiment cases on Table 2.

JPEG Compression is one of the common compres-

sion attacks on digital images (Woo et al., 2005). Re-

sizing is another common attack on digital images. In

addition, combining Resize with JPEG Compression

is also considered. Figure 6 shows a sample RIC of

experiment case No.9 on Table 2. Table 3 shows the

result of experiment cases.

Table 2: Robustness Experiment Case.

No Attack Type

1 No Attack

2 50% JPEG Compression

3 30% JPEG Compression

4 Resize to 2000px

5 Resize to 2000px × 50% JPEG Compres-

sion

6 Resize to 2000px × 30% JPEG Compres-

sion

7 Resize to 420px

8 Resize to 420px × 50% JPEG Compres-

sion

9 Resize to 420px × 30% JPEG Compres-

sion

10 No. 9 × Resize to 1280px × 30% JPEG

Compression

11 No. 10 × Resize to 450px

12 No. 11 × 30% JPEG Compression

13 No. 12 × Resize to 2000px

14 No. 13 × 30% JPEG Compression

15 No. 14 × Resize to 400px

16 No. 15 × Resize to 420px

In the experiment cases, RIC of images over

420px Resize were detected successfully. Also, the

Figure 6: Resize 420px and 30% JPEG Compression of Fig-

ure 3.

size average of doubt list converged to 1, which

proves that RIC decoder detects mostly only one orig-

inal data with the doubt color correction algorithm.

On the performance evaluation of RIC decoder, it

took less than one second. Table 2 and 3 show that

decoding time is in direct proportion to image’s size

and degradation.

4.3 Comparing with Related Work

In this experiment, there are seven attack types used

to compare the proposed system with QR Code (In-

corporated., 2008) and Robust watermark (Yesilyurt

et al., 2013). Three methods embed a database in-

dex into an image. QR Code, embedded a database

index with the high error correction level, is embed-

ded on an image’s left-bottom area with same pixel

size of RIC. On the other hand, on Robust water-

mark, the two images of 400px and 640px are ﬁlled

with a redundant index by the DCT Based watermark-

ing method using Luminance component. The result

is regarded as success when an extracted data is the

same as an embedded data. The experiment process

is cited as follows. Table 4 shows the result of this

experiment.

• Step 1 : Generate a random database index.

• Step 2 : Pick an image and embed the generated

index to it by QR Code, Watermark and RIC.

• Step 3 : Attack embedded images by Resize or

JPEG Compression.

• Step 4 : Extract the index from attacked images

and check matching up with the original index.

It can be seen on Table 4 that the embedded data

using QR Code or Robust watermark do not have

robustness on Resize and JPEG Compression. On

the other hand, the data embedded by our proposed

method is correctly extracted over the 420px. Hence,

RIC is the only image-embed method to digital im-

ages on the Internet.

SIGMAP 2016 - International Conference on Signal Processing and Multimedia Applications

Table 3: Robustness Experiment Result.

No RIC Size(px) PSNR Avg Success Rates Size Avg of Doubt List Decode Time Avg

1 204×205 ∞ 1.0000 1.0000 0.0630s

2 204×205 33.76 1.0000 1.0000 0.0562s

3 204×205 32.22 1.0000 1.0000 0.0572s

4 313×314 42.33 1.0000 1.0000 0.0810s

5 313×314 37.58 1.0000 1.0000 0.0781s

6 313×314 35.81 1.0000 1.0000 0.0715s

7 73×74 31.74 1.0000 1.0000 0.0176s

8 73×74 29.47 1.0000 1.0000 0.0649s

9 73×74 28.64 1.0000 1.0000 0.4000s

10 204×205 27.71 1.0000 1.0000 0.4200s

11 77×78 27.50 1.0000 1.0000 0.4301s

12 77×78 27.47 1.0000 1.0000 0.4152s

13 313×314 27.22 1.0000 1.0000 0.4872s

14 313×314 27.10 1.0000 1.0000 0.4565s

15 70×71 28.02 0.9999 1.0000 0.4210s

16 73×74 27.90 0.9998 1.0000 0.7228s

Table 4: Comparison Experiment Result.

Attack Type

QR Code (Incor-

porated., 2008)

Success Rates

Watermark (Yesi-

lyurt et al., 2013)

Success Rates

(400px)

Watermark (Yesi-

lyurt et al., 2013)

Success Rates

(640px)

Proposed Method

Success Rates

No Attack 0.9700 1.0000 1.0000 1.0000

Resize to 450px 0.7421 0.9999 1.0000 1.0000

Resize to 450px ×

30% JPEG Comp.

0.5291 0.0594 0.0000 1.0000

Resize to 420px 0.0926 1.0000 0.9978 1.0000

Resize to 420px ×

30% JPEG Comp.

0.2723 0.0278 0.0000 1.0000

Resize to 400px 0.9416 1.0000 0.9998 1.0000

Resize to 400px ×

30% JPEG Comp.

0.5469 0.0131 0.0000 0.9999

5 SUPERDISTRIBUTION

SYSTEM

Superdistribution system (Mori and Kawahara, 1990)

is an ideal proposal to distribute digital products in

which software is made available freely and with-

out restriction but is protected from modiﬁcations and

modes of usage not authorized by its vendor, and nor-

mally there are three principal functions (Wikipedia,

2013) :

• A Cryptographic wrapper for digital products that

cannot be removedand remains in place whenever

the product is copied.

• A digital rights management system for tracking

usage of the product and assuring that any usage

of the product or access to its code conforms to

the terms set by the product owner.

• An arrangement for secure payments from the

product’s users to its owner.

The idea above is the most optimized architecture

for both product owners and recipients for the reason

that there is no restraint or additional expense through

several distribution channels. Unfortunately, the com-

plete concept of Superdistribution systems barely ex-

ists in the reality, but there are several partial Su-

perdistribution systems which intend to make a mas-

sive proﬁt from the distribution process on the Inter-

net. In other words, massive platform companies have

built up their own ecosystem to make a proﬁt from

both product owners and recipients. For example, Ap-

ple app store is a huge cash cow for Apple taking a ﬂat

Robust Index Code with Digital Images on the Internet

Product

Owner

<Embed digital products

into an image>

<Cryptographically

wrap>

RIC Image

Database

index

<Recursively

spread out>

Internet

service

RIC Decoder

Extract the

embedded

index

Fetch the

original

product data

Recipient

Embed the

database

index using

RIC

Show the

original

data

<Pay directly

to the owner>

RIC Encoder

Save digital

products and

image’s

inclination

Embed the

database index

into the image

Remote Server

Remote

Database

II III

Fetch a

database

index

Figure 7: New Superdistribution System Architecture.

30% cut of all App Store transactions, earning Apple

over 10 billions USD in revenue in a year (Applein-

sider, 2014).

To the best of our knowledge, there has not been

a complete Superdistribution system. To design the

desired system, we need the new source as a Crypto-

graphic wrapper which is able to embed safely both

digital products and security, payment arrangement

from a product owner, and barely damaged by all the

expected degradation on the Internet, and also recur-

sively spread out through Internet services.

For the reasons above, we considered three basic

conditions for the wrapper of the new Superdistribu-

tion system. The digital image has received signiﬁ-

cant attention for the ﬁrst condition, and the concept

of RIC has been designed for the second and third

condition in the ﬁrst place.

• Intriguing sources for recipients, which spread out

easily, regardless of platforms

• An almost unlimited capacity for diverse types of

contents, and low risk for security issues

• Extremely high robustness against degradation on

distribution process on the Internet

Product owners embed their digital products, se-

curity or payment policy into a digital image by RIC

Encoder. RIC Encoder preferentially extracts incli-

nation which shows the features of the digital image,

such as Brightness, Histogram, Ratio, Keypoints and

so on, and save them with digital products into a re-

mote database. Subsequently, the index of the address

to access the data is embedded on the image by RIC.

The new embedded image is called RIC Image, and

used as a Cryptographic wrapper in this architecture,

shown in Figure 7.

The RIC image is exposed to recipients by being

recursively copied and spreading out on the Internet

by existing Internet services like SNS. Afterwards the

recipients are able to extract the products, security or

payment policy of product owners easily, if RIC De-

coder Library is implemented in their devices. More-

over, even if a codemark is deleted from an image, the

original products remain on a remote database so that

recipients are able to access the data from other RIC

images.

Compared to the existing distribution process, the

main difference is that all the data which productown-

ers intend can be easily distributed, regardless any

existing platforms, and recipients consume the data

by the policy arranged from the product owner, not

platformers, because the image contains the products.

For instance, talented artists embed their music con-

tents and concert information of themselves into the

their attractive images, which means they get RIC im-

ages linked with their contents. The thing they only

have to do for advertising themselves is uploading

RIC images to SNS like Facebook, Twitter, LINE,

KakaoTalk, WhatsApp and so on. Users who ﬁnd the

RIC image can easily access to the original contents

like right clicking the image on the Google Image

Search on PC. Hence, RIC is the only method target-

ing digital images on the Internet so that the new pro-

posed Superdistribution system can be only realized

by RIC. When the RIC library becomes a middleware

on the Internet construction, the proposed Superdis-

tribution system will be realized.

6 CONCLUSION

This paper presented the design and implementation

of the extremely high robustness codemark technol-

ogy, named RIC. We have determined that a digital

image is one of the most desirable sources, for reasons

that users already get used to use it for communica-

tion, and it is relatively easy to be copied and shared.

RIC has the apparent advantage to link a digital im-

age with unlimited digital data, and also guarantees

most cases of degradation occurred on the Internet, so

SIGMAP 2016 - International Conference on Signal Processing and Multimedia Applications

that the strengths of digital images above can be max-

imized. Our experiments validate the robustness of

RIC.

To our knowledge, there is no digital image ser-

vice using embedding data technologies into images.

RIC is mainly designed for digital images for chal-

lenging to open a new Superdistribution system, com-

pared to the existing ecosystems provided by platform

companies. Using RIC, it is possible that RIC images

can spread out in any sharing way, such as E-mail,

SNS, chat-app, and the original embedded digital data

can be easily extracted by end recipients on any Inter-

net services, regardless of platforms. We had already

implemented RIC to iOS, Android, Chrome browser

extension, and so on to conﬁrm that RIC can be oper-

ated across diverse platforms.

In spite that high robustness of RIC is realized,

the performance of RIC, such as extracting time is

not completely considered. For example, the doubt

color correction algorithm helps detect damaged im-

ages, but it is highly likely that decoding process of

terribly damaged images takes relatively long time

when N

is big. We keep trying the way of optimizing

a doubt color correction algorithm with a small value

of N

. In addition, research for image’s inclination

matching is also critically important for security pol-

icy, and we are still trying several additional image

matching features. These future works will upgrade

the completeness and stability of the system, which

also means RIC will possibly be able to be operated in

the worse conditions than the currently deﬁned speci-

ﬁcation.

ACKNOWLEDGEMENTS

This work was supported by Tokyo Institute of Tech-

nology and Pulit Inc. The authors would like to ap-

preciate the anonymous reviewers for their valuable

suggestions and comments.

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APPENDIX

This appendix describes an example of the prototype

iOS application of the RIC library. Figure 8 shows a

RIC image linked with the food information. When

users open the image in the app, they can see gray

spots which contain information like Figure 9. With

tapping a spot, food name, description, location and

so on will be displayed shown in Figure 10.

Figure 8: Example of the RIC image.

Figure 9: Open Figure 8 in the Application.

SIGMAP 2016 - International Conference on Signal Processing and Multimedia Applications

Figure 10: Detail information of the RIC image in the Ap-

plication.

Robust Index Code with Digital Images on the Internet