Product Piracy Prevention: Product Counterfeit Detection without Security Labels

8/13/2019 Product Piracy Prevention: Product Counterfeit Detection without Security Labels

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Product Piracy Prevention: Product Counterfeit Detection Without Security

Labels

Christian Horn1, Matthias Blankenburg2 and Jorg Kruger2

1Department of Industrial Automation Technology at Berlin Institute of Technology2Fraunhofer Institute for Production Systems and Design Technology

[email protected], [email protected], [email protected]

ABSTRACT

Counterfeiting is a challenge to companies, cus-

tomers and markets all over the world. Besides

the economic damage which affects in particular

the companies and countries that use advanced pro-

duction and manufacturing processes based on in-

tensive research and development to produce high

quality goods, safety standards are omitted. These

standards protect usually the cutomer from goods

which are dangerous or harzardous to health.

Product piracy prevention is often followed by

the application of RFID tags to supervise supply

chains. The lack of robust counterfeit detection

methods created a market for artificial security la-

bels which are used to secure the product itself.

The specific conditions of production, manufac-

turing technologies and materials generate specific

features, which identify every product uniquely.

The innovation of this text is the detection of these

features in an automated fashion through the com-

bination of digital sensing and machine learning,

rendering the application of artificial security la-bels obsolete.

KEYWORDS

Automated Counterfeit Detection, Product Finger-

printing, Pattern Recognition, Sensor Fusion, Clas-

sification

Figure 1: Cases of customs enforcements of intellec-

tual property rights at the european border, from [1]

1 INTRODUCTION

Figure 1, taken from the annual Report on

EU customs enforcement of intellectual prop-

erty rights of the European Union in 2012

[1], shows a continious upward trend in the

number of shipments suspected of violating

intellectual property rights for the last years.

In 2011 more than 90 thousand cases of de-

tained articles were reported. The value totheir equivalent genuine products is estimated

to be over 1.2 billion euro and this covers only

Europe.

To get an idea of the worldwide amount of

economic damage for the last years the re-

port The Economic Impact of counterfeit-

ing and piracy [2] of 2008 estimates a total

loss of 250 billion dollars in the year 2007.

International Journal of Cyber-Security and Digital Forensics (IJCSDF) 2(1): 88-102

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The Society of Digital Information and Wireless Communications, 2013 (ISSN: 2305-0012)


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This report covers the analysis of international

trade in counterfeit and pirated products, but

these estimates do not include domestically

produced and consumed counterfeit and pi-

rated digital products being distributed via the

Internet. If these were also considered, the

magnitude of counterfeiting and piracy world-

wide could be several hundred billion dol-

lars more in 2007. Furthermore, if we com-

pare these numbers to the amount of cases re-

ported in Figure 1, they probably doubled in

2011. The effect of counterfeiting and piracy

is an intermission of innovation and thus im-

pairment of economic growth. The economic

damage affects in particular countries that use

advanced production and manufacturing pro-cesses based on intensive research and devel-

opment to produce high quality goods.

Another very important argument to enable

the differentiation between brand products and

their counterfeits is safety. It is stated in the

OECD report that the products counterfeiters

and pirates produce and distribute are often of

minor quality and can even be dangerous and

health hazards. Common standards that ensure

the safety of products can be ignored by prod-uct pirates and the used materials can be dan-

gerous.

With the magnitude of counterfeiting and

piracy in mind, these reports emphasize the

need for more effective enforcement to com-

bat the counterfeiting and piracy on the part

of governments and businesses alike. A key

component for this enforcement is the devel-

opment of new methods for automated coun-

terfeit detection.The review of copyright infringement of reg-

istered trademarks and products is not easy to

implement. Due to the high number of pend-

ing trademarks and constantly added new ap-

plications it is very difficult for the executive

bodies, such as customs, to register violations

of trademark rights immediately and in a com-

prehensive manner. The awareness to all reg-

Figure 2: Current scenario for counterfeit detection

through customs officials

Figure 3: Desired scenario for counterfeit detection

through customs officials

istered brands and products is for the execu-

tive organs not possible and therefore neces-

sarily, trademark infringement remains unno-

ticed. The current scenario for products enter-

ing a market in a foreign country is diplayed inFigure 2. Here it is shown how customs offi-

cials usually handle the inspection of products

at the border. First the goods arrive at a spe-

cific check point, usually via sea- or airfreight.

If the customs officer notices some anomaly in

the paperwork, he will check the cargo con-

tainers. As discussed earlier the officer is

often not an expert for the shipped product,

so he could not detect a counterfeit. Instead

the company producing the genuine product iscontacted to send their own expert, which can

verify the product. This is a time-consuming

and expensive process, therfore most contain-

ers in question often remain unnoticed.

To overcome these limitations in the checkup

routine an automated expert-system is neces-

sary that can support the customs officials, as

shown in Figure 3. Given that the officer could


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Figure 4: Categories of counterfeit products, from [1]

verify the shipped cargo by himself while the

company issues the authentication system for

their products. This idea was adopted more

recently through an application of artificial se-curity features to products. The issues of such

security labels are in part the high cost, and

additionally the integration into the product.

On the other hand high-quality branded prod-

ucts, as the target of counterfeiting, have usu-

ally, due to the production processes and ma-

terials used, and in view of its processing

machinery and equipment, a grade of high

quality. The specific conditions of produc-

tion, manufacturing technologies and materi-als generate specific features, which identify

the product uniquely. These features may be

detected multimodal by man, including tac-

tile (plasticity, elasticity, thermal conductivity,

surface structure), visual (shape, colour, sur-

face texture, transparency), olfactory (smell)

or acoustic (sound) perceptions. In general,

only the person familiar with the manufac-

ture of the product can combine these inher-

ent characteristics in their entirety so that itcan differentiate the genuine product from a

clear counterfeit. The innovation of this text

is the detection of these features in an auto-

mated fashion through the combination of dig-

ital sensing and machine learning, rendering

the application of artificial security labels ob-

solete. As shown in Figure 4, more than 60%

of counterfeit products are shoes, bags and

clothing. Therfore two properties of a prod-

uct have been identified as the most promising

ones suitable for identification: the olfactory

and the optical features.

2 STATE-OF-THE-ART-

TECHNOLOGY

Common automated counterfeit detection

methods require nowadays additional security

features at the product itself. Several methods

have been developed, but main advantages and

disadvantages remain similar.

Additional security features require further

steps in production to add these features to the

product. This raises expenses, manufacturing

time and development efforts, which is clearly

a disadvantage. On the other hand the security

is enhanced and an original brand is easy to

detect in an automated fashion, since there is a

specific feature to look for. But this could also

be a main disadvantage, if the security feature

itself is easy to reproduce and could be added

to any forged product. Another challenge is

to link the securtiy label to the brand product

in a way it cannot be removed or stolen. Thisway product pirates could label their counter-

feits easily as an original with an original se-

curity label.

Figure 5 shows examples of different labels

which are commonly used on products for dif-

ferent purposes. One purpose is the use as a

logical security feature where the security la-

bel contains unique information and cannot be

copied. Counterfeit detection without artificial

security tags is a solution to these problems,if the counterfeit is distinguishable from the

original brand.

2.1 Security Labels

The report [3] of the German Engineering Fed-

eration shows the latest innovations against

product piracy. It gives a comprehensive


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(a) QR-Code (b) RFID

(c) Fraunhofer

SecuriFlex

Figure 5: Different types of security labels

overview of the latest efforts in product pro-

tection. A reasonably well studied approach

is the extensive supervision of supply-chains.

Here the application of RFID tags plays a sig-

nificant role, as the latest form of artificial

security tags, which can easily be integrated

with existing logistic chains. The applica-

tion of Data Matrix Codes (DMC) is discussed

as well as a cost-effective alternative. Much

work has been done to link these tags insepa-

rably with the corresponding product to hinderproduct pirates from transferring these tags to

their counterfeits. But in general it is observed

that this protection method holds only with

tremendous logistic implications, since todays

products cover various stations during the dis-

tribution process. Up to now there has been no

common standard available and the customs

authorities integration is still open. Even

when the cost of these artificial tags could be

reduced by advances in the production pro-cess, as e. g. the introduced direct printing of

RFID antennas onto packaging, additional ex-

penses with no direct use for the customer will

arise. Security Tags like holograms found at-

tached to various consumer goods give nearly

no protection against counterfeiting since ma-

chine readability is poor and knowledge of the

correct appearance is scarce.

2.2 Product-Inherent Features

The Inherent ID Project adopts a novel ap-

proach to protecting high-value products from

counterfeiting. The approach is based on thestationary and mobile capture of key prod-

uct features indissolubly linked with the prod-

uct which enable its production process to be

traced. This not only renders obsolete the ap-

plication of security tags but also gives en-

hanced protection against counterfeiting as the

inherent characteristics that the high-quality

production process impregnate in the genuine

product are combined with one another to

serve as proof of product identity. They form

the basis on which electronic certificates of au-

thenticity can be issued without the need for

complicated explicit security markings. Meth-

ods for the capture and control of identity char-

acteristics are being elaborated in the Inherent

ID project for system integration using intel-

ligent cameras and an electronic nose. The

identity characteristics captured by this range

of sensors serve both for the product identi-

fication and product authentication. At the

same time this also offers opportunities for im-proving documentation of product flows in the

supply chain. Full documentation serves as a

complement to the inherent characteristics of

the authentic product and offers valuable in-

formation of verification of the genuine arti-

cle, thus serving to safeguard against counter-

feits. The Project aimes to answer the ques-

tion: Which inherent features allow separation

of genuine products from counterfeits in an au-

tomated fashion? The motivation of this ques-tion is the assumption that genuine products

must differ in its properties from its counter-

feit, since the product pirate tries to maximize

its profit by using material of inferior quality

and misusing a trademark of a genuine man-

ufacturer to feint the customer. One result of

the project is that only a combination of fea-

tures can detect counterfeits at a decent rate


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Figure 6: Concept of the Project Inherent ID

for different products.

3 MATERIALS AND METHODS

Optical 2D and 3D characteristics as well as

olfactory characteristics are combined with

one another to serve as proof of product iden-

tity, as shown in Figure 6. They form the basis

on which electronic certificates of authentic-

ity can be issued without the need for com-

plicated explicit security markings. The iden-tity characteristics captured by this range of

sensors serve both for product identification

and product authentication. At the same time

this also offers opportunities for improving

documentation of product flows in the supply

chain. Within the scope of Inherent ID is the

successful establishment of a laboratory pro-

viding multi-modal measurement equipment

comprising multigas sensor array for olfactory

analysis, high resolution camera for textureanalysis and stereo vision, as well as range

cameras for 3D feature extraction. Further re-

search is conducted with the aim for increas-

ing robustness of the sole test methods espe-

cially under ambiguous environments, integra-

tion into portable devices, implementing sen-

sor data fusion for increased detection ratio,

effortless integration into supply chains and

developing efficient data models for storage

of various features depending on the regarded

product.

3.1 Texture Features

The ability to characterise visual textures and

extract the features inherent to them is consid-

ered to be a powerful tool and has many rel-

evant applications. A textural signature capa-

ble of capturing these features, and in partic-

ular capable of coping with various changes

in the environment would be highly suited to

describing and recognising image textures [6].

As humans, we are able to recognise texture

intuitively. However, in the application of

Computer Vision it is incredibly difficult to de-

fine how one texture differs from another. In

order to understand, and manipulate textural

image data, it is important to define what tex-

ture is. Image texture is defined as a function

of the spatial variation of pixel intensities [5].

Furthermore, the mathematical description of

image texture should incorporate, identify and

define the textural features that intuitively al-

low humans to differentiate between differ-ent textures. Numerous methods have been

designed, which in the past have commonly

utilised statistical models, however most of

them are sensitive to changes in viewpoint and

illumination conditions [6]. For the purposes

of mobile counterfeit detection, it is clear that

this would be an important characteristic for

the signature to have, as these conditions can

not be entirely controlled. Recently a descrip-

tion method based on fractal geometry knownas the multifractal spectrum has grown in pop-

ularity and is now considered to be a use-

ful tool in characterising image texture. One

of the most significant advantages is that the

multifractal spectrum is invariant to the bi-

Lipschitz transform, which is a very general

transform that includes perspective and texture

surface deformations [6].


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Figure 7: Workflow for generating a textural signature

Another advantage of Multrifractal Spectra is

that it has low dimension and is very efficient

to compute [6] in comparison to other methods

which achieve invariancy to viewpoint and il-

lumination changes such as those detailed in

[7], [8]. One of the key advantages of mul-

tifractal spectra, which is utilised here is that

they can be defined by many different cat-

egorisations or measures, which means that

multiple spectra can be produced for the same

image.

This is achieved through the use of filtering,

whereby certain filters are applied to enhance

certain aspects of the texture, to create a new

measure. Certain measures are more or less in-

1 1.5 2 2.5 3 3.50

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Local fractal dimension

Globalfractaldimensionf()

Gaussian

Gradient

Laplacian

1 1.5 2 2.5 3 3.50

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Local fractal dimension

Globalfractaldimensionf()

Gaussian

Gradient

Laplacian

Figure 8: Multi-Fractal-Spectra of texture of a textile

product (top) and its counterfeit (bottom)

variant to certain transforms, and the combina-

tion of a number of spectra achieves a greater

robustness to these. The worklfow is depicted

in Figure 7 and an example is given in Fig-

ure 8.

3.2 Shape Features

Since manual detection is often done visual

by customs officials, visual features are also

important for any automatic detection mecha-

nism. Besides detecting features through two

dimensional image processing, three dimen-

sional data capture is necessary for counterfeit

detection, because it provides important addi-

tional information.

To capture a real-world object in three di-mensions a 3D scanner, or range camera, can

be used. The basic principles of 3D scan-

ners available on the market are triangulation,

time-of-flight or interferometric approaches,

whereas each principle has its advantages or

disadvantages. For a profound insight into that

topic refer to [9]. We use a mobile structured-

light 3D scanner for our application, but in


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Figure 9: Scanned shoes from our database

general any three dimensional data acquisition

method can be used to capture a real-world ob-

ject. But while using different kinds of scan-

ning techniques the results may vary.

One distinguishable feature of brand products

is the shape itself. Shape matching is a well

studied topic and several publications can be

found over the last 15 years. Despite many

different approaches available, most practicalapplications still use the 1992 introduced Iter-

ative Closest Point Algorithm (ICP) [10] or its

optimized variants to match objects. This is

due to the fact that most newer approaches are

neither easy to implement nor able to run at

a reasonable speed for the use in commercial

software.

One major challenge for three dimensional ob-

ject capture is the huge amount of data that has

to be processed. The 3D scanner we use has anaccuracy of 20 to 50mand generates around

300, 000vertices per object. Assuming a pointper point matching algorithm with O(nc) andc > 1 growth rate and a calculation time of1ms per point match, it would take nearly 3years to calculate a match of two objects.

Feature-based approaches have become very

popular since some years in image analysis

(2D) due to robustness and less computational

effort compared to other approaches. In shapematching (3D) feature-based approaches have

been introduced more recently and are gaining

popularity in shape retrieval applications for

the same reasons. The major difference among

these is whether the approach uses global or

local features. A global feature describes the

whole object, while local features only de-

scribe parts or details of an object. In [11] an

(a) 1/f-Noise (b) White Noise

Figure 10: Different kinds of noise applied to a mesh

overview of shape matching principles and al-

gorithms can be found.

Many shape matching approaches use digital

human made data like the Princeton-Shape-

Benchmark [12] or the SHREC datasets

[13] to evaluate their algorithms. Scanned

data from real world objects is different to

arificially-made data in a sense that holes1

andvariations between two scans of the same ob-

ject can appear. The SHREC datasets have

indeed several categories with different 3D

models to mirror these real-world challenges,

but the categories are examined separately and

models are artificially-made too. For that

reason we created our own database using

3D scanners and our students shoes. Fig-

ure 9 shows some examples of our scans. We

scanned some shoes with different scanners to

get a more complex testing database. Further-

more different types of noise were applied to

the scanned models as shown in Figure 10.

Approaches using global features are not suit-

able for counterfeit detection, where minor

details of an object can be highly important.

1Holes are areas on the scanned object where the

used scanning technique has troubles to capture data.


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Figure 11: Laplacian Smoothing

Therefore only approaches detecting local fea-

tures were taken into consideration. Our auto-

matic local-feature-based matching algorithm

consists of two major parts: a feature detector

and a feature descriptor. The classification is

done later after the texture and odour features

are combined with the shape features trough

feature fusion.

Feature Detector

The feature detector finds points of inter-

est on a given mesh which are usually ex-

trema in a specific mathematical notation. In

two-dimensional approaches well known tech-

niques like corner detection are used. In three

dimensions new approaches based on two-

dimensional image processing algorithms that

use feature-based approaches have been de-

veloped. Examples are the Harris-3D-feature

detector [14], several portations of the SIFT-

algorithm to three dimensions [15, 16] or

the 3D equivalent of SURF [17]. Other

approaches use for example Heat-Kernel-

Signatures [19] or maximally stable extremal

regions (MSER) [18] to detect features.

For countereit detection we use a Scale Space

approach to detect keypoints [9]. The Scale

Space is usually constructed by repeatedly ap-plying a filter to a given mesh.

L(x , y , z , ) =F(x , y , z , ) M(x,y,z)

whereas M ist the mesh and F ist the filter-

kernel. The difference of the resulting meshes

is then examined for extremas. As filter-

kernel a finite difference approximation of the

Figure 12: Key Points for two Scans of the same Shoe

Laplace operator

G(x , y , z , ) = 1

n

n

i=1

iPi

was used, where is a weighting factor and

Pi are the neigbors of the regarded point. The

advantage of this smoothing approach is that

each point keeps is relative position, keepingthe shape itself of the whole object, as shown

in Figure 11. The differences of the mean cur-

vature at each point is the criterion for con-

structing the Scale Space. Figure 12 shows

detected keypoints of two differnt scans of the

same shoe.

Feature Descriptor

The feature descriptor transforms the area atthe detected keypoint into an easy compa-

rable and meaningful description. Usually

the approaches combine feature detectors and

feature descriptors into one method. Well

known methods like MeshSIFT [16] or 3D-

SURF [17] use their three-dimensional coun-

terpart of feature descriptors developed for

two-dimensional applications. Approaches

using Heat Kernel Signatures [19, 20] use

these for both detection and description. Incontrast to that another approach called Spin-

Images [21] is a feature descriptor only. It is

able to describe an object locally or globally.

In [22] this concept was adopted to a scale-

invariant version encoding local information.

Figure 13 shows a transformation of the area

surrounding keypoints into a 2D dense map

using Spin Images [21]. Here a 3D mesh is


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Figure 13: Transformation of shape features

transformed into several 2D maps, each re-

lated to a keypoint

SO : R3 R2

The 2D dense map ist constructed usting the

equation

(, ) = (x p2 (n (x p))2, n(xp))

where(, )describe the new 2D coordinates.It is a cylindric coordinate system with its

point of origin in the regarded point of the

mesh. A set of ranked Spin Images describes

the object itself, so it can be matched to the

abstract brand model.

Figure 14 summarizes the required steps for

our shape matching algorithm using real world

objects. The shape matching algorithm re-

quires a three dimensional model of the prod-

uct as input which can be matched to an ab-

stract model of the brand product. The abstract

model is a description of features that render

the brand unique.

3.3 Odour Features

Much effort has been spent on how odour

could be measured. The European Standard

EN-13725 [23] defines a method for the objec-

tive determination of the odour concentrationof a gaseous sample using so called dynamic

olfactometry. It is currently the only standard-

ized method for the evaluation of odour im-

pressions.

The dynamic olfactometry is a method where

a panel of human assessors evaluates the con-

centration of odour in a series of standard-

ized presentations of a gas sample. Here the

Construct

Scale Space

Detect Key-Points

in Scale Space

User Input/

3D Model of Object

Shape Signature

Compute Local

Spin Images around

Key-Points

Compute Signature

from Spin Image Sets

Figure 14: Workflow for generating a shape signature

emission rate of odours emanating from point

sources, area sources with outward flow and

area sources without outward flow are consid-

ered. The primary application of this standard

is to provide a common basis for evaluation

of odorant emissions in the member states of

the European Union. Every method claiming

the ability to detect arbitrary odour emissions

has to benchmark against this standard. Anoverview of the development and application

of electronic noses is given in Gardner and

Bartlett [24].

In general it was observed that electronic

noses do not react to human inodorous gases

and were also unable to detect some gases

humans are able to smell naturally. Begin-

ning with the working principle of specific gas


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Figure 15: Olfactory pattern of a genuine jersey (top)

and a counterfeit (bottom)

sensors the concept of electronic noses as acombination of sensor array and diverse pat-

tern recognition algorithms for classification

is introduced. In principle the sensor con-

cepts could be divided into three categories.

The commercially available electronic nose

Artinos basing on the KAMINA (KArlsuher

MIkroNase) [25] is a representative of metal

conductance sensors. Here the sample gas

flowing alongside the sensor surface is chang-

ing the concentration and configuration of ox-ide containing compounds, thus changing the

conductance of the metal-oxide, which is then

used as a measurement signal. The sensor

elements differ by the thickness of silicon

dioxide coating. Additionally the temperature

is changed over time producing 38 analogue

channels containing also transient responses,

which are to be analysed. Due to its working

principle these sensors deliver the most unspe-

cific data, which is both an advantage and a

disadvantage at the same time, since the sen-

sors are suitable for a broad variety of samples,

but the signal processing is harder to realise.

A metal-oxide conductance sensor using 16

channels was utilized in the project Inherent-

ID [4].

A similar sensor setup is used in [26], the

difference being that the sensor elements are

coated with different polymers, which induce

a change in conductance to specific gas com-

ponents. It was shown that with four differ-

ent sensor types held at four different temper-

atures, so a total of 16 channels and follow-

ing linear discriminant analysis ovarian can-

cer could be detected from tissue samples.

There are still some issues with falsely re-

jected samples, but the results were quite im-

pressive with respect to the use of ad-hoc

methods. Another sensor concept utilising

polymer coatings are the quartz microbalance

sensor arrays as described in [27]. These sen-

sors detect the change of frequency when a

gas is flowing over the sensor surface. In

principle these arrays are very sensitive butalso very susceptible to disturbances. Most

of recently published results in odour detec-

tion are based on linear discriminant analy-

sis and derivatives thereof. These methods

are efficient in classification of complex sen-

sor data, but with a manageable number of

classes. And these methods need a significant

amount of data present and are therefore not

suitable for the here elaborated problem of one

to many matching, as needed for the applica-tion in counterfeit detection. An additional ob-

stacle is the sensitivity to ambient conditions

which result in wide variance of measurement

data from the same class of samples. Effort

is made in the extraction of relevant features

for the purpose of reducing the dimensional-

ity and the suppression of ambient influences

which was done by independent component

analysis. An attempt of designing a general

odour model was made in [28], but was not

successful due to the sensors used and the fact

that nonlinear behaviour was excluded in ad-

vance. So the usage of specific models is more

promising.


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Desired Signal Extraction

As it was described in the previous chapter

there are many ambient influences to odour

sensing. For example humidity and temper-

ature are different in Germany and Malaysia.Additionaly a mathematical expression for

the composition of odour is not linear, so

odourous influences cannot be filtered out eas-

ily. Given these facts and that the used Artinos

Sensor returns most unspecific data it is a chal-

lenge to filter environmental influences.

Figure 16: Independent Components of the measure-

ments of a test object (left) and the environment (right)taken with an 16-channel multi gas sensor array, arb.

unit

To meet the challenge of extracting desired

signals in a robust fashion and filter the en-

vironmental noise we use a similar approach

to blind source separation, where two different

measurements are conducted. The first one is a

pattern from the environment without test ob-

ject. The second is a pattern from the desired

sample in the beforementioned environment.

The first signal can then be used to extract the

plain odour of the object itself from the sec-

ond signal. The components can be identi-

fied and thus the ambient influence can be fil-

tered. Since the electronic nose measurement

data delivers a nonlinear mixture of the envi-

ronmental and sample odour there is no obvi-

ous connection between these two patterns.

One approach to divide the signals into their

components is the Independent Component

Analysis (ICA). Here the seperation is done

by statistical means. At most the ICA can re-

turn as many independent components as thenumber of sensors used for capturing the input

data, whilst reducing the complexity. In gen-

eral the ICA has two major problems. The first

problem is that the independent components

are permuted. The sequence of two algorith-

mic cycles might not be the same even with the

same data. The second problem is the loss of

variance information in the independent com-

ponents, since it cannot be restored.

Figure 16 shows the independent componentsof a textile sample pattern on the left and the

environmental reference pattern on the right

side. The independent components were ex-

ctracted by an extended Bell & Sejnowski Al-

gorithm [29] with adjusted break condition.

Here the covariance criterion [30] was used.

E{g(u)uT}= I

If this equation is true the gi(yi)andyj are un-

correlated for i = j. Therefore this can beseen as a nonlinear variant of principal com-

ponent analysis.

The next step after the ICA is to check the in-

tegrity of the independent components. There

are a some independent components which

seem to be noise. An autocorrelation analysis

identifies a possible noise contribution. These

independent components can be omitted. Af-


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Figure 17: The Independent Components with reason-

able high similarity measure are indicated by arrows.Noise contribution was omitted. arb. unit

terwards the similarity between the sample

and the environmental independent compo-

nents are evaluated by applying the cosine dis-

tance. The results are shown in Figure 17. The

independent components with the strongest

connection are the independent components

which represent the environment in the sam-

ple data and could be omitted as well. The ar-rows in Figure 17 indicate the corresponding

independent components.

The signals which are exclusive to the sample

measurement represent the the core informa-

tion on the odour of the test object. Figure 18

shows the desired signal patterns which are ex-

clusive for the textile sample.

4 WORKFLOW

With the features described above there is a

strong basis for automated classification of

patterns. The key point for a robust and re-

liable counterfeit detection is the combination

of these features and additional user informa-

tion with the aim to derive a decision wether

the probe is likely to be a counterfeit. An ad-

vantage of the proposed algorithms for feature

Figure 18: Core information of a textile sample, arb.

unit

Figure 19: Concept of feature fusion

extraction is the possibility to utilize statistical

frameworks since the features are represented

by probability density distributions.

In general there are various approaches possi-

ble. Starting with a direct fusion of the fea-

tures as proposed in [31] and shown in Figure

19, or a more sophisticated approach whichis taking the process of probing into account.

Such a workflow is depicted in Figure 20.

Here the decision process is not necissarily

based on the utilization of all features, since

some of them are dispensible or could be mis-

leading. Think of the probing of shirt, obvi-

ously the 3D geometry cannot give a relevant

contribution to the decision process and the


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Figure 20: Sophisticated workflow for counterfeit de-

tection

3D scanning can therefore be ommitted. The

classification itself is done with an adjusted

Bayesian approach where special account was

given to the detection of novel and therefore

unknown patterns. This was done with estima-

tion of the Level of Signifcance distribution,

which gives a decision information and an ad-

ditional value of the plausibilty of this deci-

sion, cf. [32].

5 CONCLUSION

It was shown that the Inherent-ID Project

adopts a novel approach to protecting high-

value products from counterfeiting. The ap-

Figure 21: Future Scenario for Counterfeit Detection

proach is based on the stationary and mobile

capture of key product features indissolubly

linked with the product which enable its pro-

duction process to be traced. This not only

renders the application of security tags obso-

lete but also gives enhanced protection against

counterfeiting as the inherent characteristicsthat the high-quality production process im-

pregnate in the genuine product are combined

with one another to serve as proof of prod-

uct identity. They form the basis on which

electronic certificates of authenticity can be is-

sued without the need for complicated explicit

security markings. Methods for the capture

and control of identity characteristics are be-

ing elaborated in the Inherent-ID project for

system integration using intelligent camerasand an electronic nose. The identity character-

istics captured by this range of sensors serve

both for the product identification and prod-

uct authentication. At the same time this also

offers opportunities for improving documenta-

tion of product flows in the supply chain. Full

documentation serves as a complement to the

inherent characteristics of the authentic prod-

uct and offers valuable information of verifi-

cation of the genuine article, thus serving to

safeguard against counterfeits.

6 PERSPECTIVE

The approach of the project Inherent ID can be

adopted to a possible future scenario for coun-

terfeit detection. As shown in Figure 21 the

approach could be ported to to work with con-


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sumer electronics like smartphones, since 3D

cameras are already available there. The textu-

ral features and the shape features of an object

could be detected with the built-in cameras.

The classification itself can then be done with

an approch using Service Orientend Archi-

tectures (SOA), where the features are trans-

fered from the smartphone over the internet to

a server. This is necessary because even re-

cent smartphones with multicore cpus are too

slow to compute the proposed algorithms in a

timely fashion.

This enables not only customs officials to de-

tect counterfeits, any customer would be able

to do that using the detection app. This could

lead to a whole new market driven combatagainst product piracy.

7 ACKNOWLEDGEMENTS

The authors would like to acknowledge the

funding of the research project Inherent-ID

by the senate of the state Berlin and the Eu-

ropean Regional Development Fund. The

project is embedded in the Fraunhofer Cluster

of Innovation Secure Identity Berlin Branden-burg. Furthermore we would like to acknowl-

edge the work done by our students Evelyn

Jungnickel, Maximilian Fechteler and Nor-

man Franke in the project.

8 THE AUTHORS

The authors are working within the Automa-

tion Group at Production Technology Center

(PTC) Berlin, Germany. The PTC comprisesthe department of Industrial Automation Tech-

nology at Technische Universitat Berlin and

the Fraunhofer Institute for Production Sys-

tems and Design Technology.

The main tasks of the Automation Group are

fundamental research and lecturing in a broad

band of topics regarding industrial automation

such as process automation and robotics, pro-

cess monitoring and simulation, image pro-

cessing and pattern recognition.

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