Towards Fully Automatic ID Document frauds detection
Clarisse MANDRIDAKE, Amine OUDDAN, Mathieu HOARAU, Kévin WIN-LIME
Keesing Research Unit, Hologram. Industries Group, 22 Avenue de l’Europe, 77600 Bussy Saint-Georges
[email protected], [email protected],
[email protected], [email protected]
Résumé – Les documents d'identité font souvent l'objet de fraudes ou sont contrefaits. Cet état de fait constitue un risque pour notre
société, aussi bien pour la sécurité des citoyens que pour l'économie du pays. Dans le cadre du projet DOCSCOPE, nous avons comme
objectif la détection automatique des traces de falsification sur les documents d’identité, et ce à partir de leurs images scannées. Durant
cette première phase du projet, nos études se sont focalisées sur l'analyse des impressions de fond du document, dont le motif peut être
considéré dans le cas général comme une texture, non-homogène. Nous avons évalué différentes approches statistiques et structurelles
permettant de caractériser ces fonds de document. Dans un deuxième temps, le cas de l’impression de la photo sera abordé où il s’agit
plutôt de texture homogène. Notre objectif étant de dégager des pistes couvrant de la façon la plus large possible les cas de fraudes aux
documents d’identité de nos jours.
Abstract – ID documents are frequently forged or counterfeited. It appears as a risk for our society, both for security and economic
reasons. In the context of DOCSCOPE project, our main goal is to provide an automatic system allowing fraud detections from scanned
image document. We have analysed during the first step of the project the possibility for automatically detecting falsification clues from
scanned ID Documents. We have investigated both statistical and structural approaches in the context of Content based Image Retrieval
and have evaluated them to Document fraud context. Our studies focus on both document’s background and photo area printing which
are considered as the most frequently way for ID document’s fraud. Our main goal would be to reach a few hints allowing automatic
document authentication covering the most frequently case of frauds.
1.
Introduction
must be made in order to cover the majority of document
frauds. This work aims to fill in this gap. We focus only
on visible light source as the basis of this work, and
efforts have been made in image quality enhancement
and high resolution image capture to guarantee better
authentication rate.
This article is organized as follows. In section 2, we will
summarize the state of the art related to ID Document
print technologies, and will introduce some detailed
information about ID document’s background printing
and photo printing, as well as their close relationship
with respectively non-homogenous and homogenous
This article deals with methods of detecting frauds based
on printing techniques of identity documents. According
to official report in the field of document frauds [1], the
most common techniques used by forgers are usually
based on bio data substitution (counterfeiting) or only
photo substitution when the other part of the document
remains authentic (falsifying). This latter is often
encountered in polycarbonate documents which is
delicate to counterfeit.
In many authentication systems, in particular Assuretec
Systems [2], a plurality of visible and invisible light
sources are used in order to capture every potential clues
or alterations in a document making it easier to
authenticate [2]. But most of them don’t include the
printing process as a part of the authentication. However
processes of reprinting either the whole document or just
a part of a document like photo substitution is an
accessible kind of frauds not all detectable from systems
available today, as reported by the 2013 Document
Challenge Workshop organized by the Research &
Development Unit of FRONTEX [3]. Indeed, it was
noticed that only 60 percent of false documents have
been detected by all the competitive Machines while 80
percent have been identified by Human Experts as
illustrated in FIG.1. This means that some document
alterations are not detected today, and improvements
1
FIG 1. Global Result from Document Challenge
Frontex
texture patterns. In Section 3 we will analyze some
characterization methods suitable for background
printing analysis, and photo printing analysis, under
main constraints as image acquisition, illumination
change, and image resolution.
2.
Overview
technologies
of
digital
Therefore, our main objective is first to identify or
recognize an offset technique against non-offset one.
And second, to evaluate classification-based methods
when 2 main classes Offset/Non-Offset could be well
separated with appropriate texture descriptors.
The two pictures above in FIG.2 are extracted from
Chinese passports. The first one is from a genuine
passport (printed by an Offset Technique) while the
second is from a false passport (printed by an Inkjet
technique). The first approaches we have studied are
based on the comparison of texture features including
both statistical and structural methods inspired by the
Content Based Image Retrieval systems [4].
In order to characterize these complex patterns
composed of fine lines/guilloches and/or printed dots
patterns, a combination of statistical and structural
features are studied. Some details of the approaches will
be proposed in section 3.
This kind of textures is called Non Homogeneous. As
we can see in these 2 examples, color distribution may
also be discriminant in the context of ID Document
fraud detection. We will present in the section 3 a brief
work based on color distribution recognition.
print
Analyzing document by its printing process can be
divided into 3 distinct parts:
 Background printing analysis
 Photo printing analysis
 Typography Printing Analysis (including
MRZ "Machine Readable Zone" and VIZ
"Variable Information Zone" analysis).
This article proposes some insights about the 2 first
parts such as: the background printing and the photo
printing.
Printing technique analysis is nothing but a kind of
texture analysis, in such a way that both background and
photo printing contain various patterns which may be
viewed as regular or non-regular pattern, fine or coarse,
directional or not, or designed-based (nonhomogeneous) patterns. Roughly speaking, these
different types are belonging to 2 groups of textures:
homogenous or non-homogenous, and have been subject
of many research topics during the last few decades. The
most popular among them are the MPEG 7 texture
descriptors [5], SURF descriptors [8], some statistical
and structural features [4], [7].
2.1
2.2
Photo Printing
We have pointed out that the background of ID
document is known to be often printed by Offset
technique. The photo printing is much more complicated
as there are 4 distinct techniques used for integrating the
photo:
o Inkjet Printing
o Electrophotographic Printing ( Laser Toner)
o Laser Engraving
o Die Diffusion Thermal Transfer
Therefore it is important to understand what
particularities each of the 4 printing techniques represent
in terms of visual properties. The knowledge of those
visual properties allows us to define what descriptors
could be efficient for each printing property, and what
classification methods could be convenient.
Against the background printing, we are here in presence
of locally Homogeneous Texture. Therefore our field of
interest will be to evaluate if classification methods
based on descriptors to be defined work with photo print
images. Some evaluation methods have been initiated
and will be described in section 3.2.
Fig. 3 shows two images extracted from polycarbonate
passports. Polycarbonate passport has the particularity of
being printed by laser engraving process, as illustrated in
the left image from a genuine document, whereas the
second image was provided by a falsified document
printed by Laser Toner scanned at 800 dpi. The
challenge will be to characterize visual “attributes”
corresponding to the 4 most techniques used for
integrating photo in ID documents.
Background printing
In many ID documents available worldwide,
backgrounds are often composed of fine lines and/or
guilloches
printed
by
an
offset
technique.
FIG 2. Background images extracted from Chinese
passports. (top) Offset Print from Genuine Document.
(Bottom) Inkjet Print from False document
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In this context, we have studied the influence of some
illumination normalisation algorithms, such as:
o Gamma correction
o Gradient Filter (Difference of Gaussian
Filter).
o Illumination Normalization
3.1.2
We have implemented several statistical approaches
based on first and second order statistics [4]:
o Gray Level Co-occurence Matrix,
o Autocorrelation Function
o Difference statistics
o Power Spectrum
o Autoregressive model
o Second order moments
FIG 3. Photo Printing: (Left) Laser Engraving,
(Right) Laser Toner
3.
Document
fraud
based on Printing Process
3.1
detection
Background printing Analysis
3.1.3
There are a large variety of ID documents. Each
country has ability to define itself its official documents
following or not the ICAO (International Civil Aviation
Organization) recommendations. As a consequence each
official document is particular in terms of security
elements it contains. Document’s Security elements refer
to materials used for manufacturing it, ranging from
inks, patterns and color distribution printed on the
substrate, additional design and logo, to all
personalization techniques and laminates including
optically variable elements.
The main difficulty in document fraud detection
based on printing technique is to identify the
characteristic of each printing process under some
constraints like image acquisition and image resolution.
The existence of holograms and other optically variable
elements in the document constitute a factor
complicating the detection system. Our first study was
based on finding suitable approaches to characterize the
background printing under these constraints. As
described in section 2.1, inspired by the CBIR system,
we started our study in exploring some statistical and
structural approaches to characterize the nonhomogenous texture that composes the document
background.
3.1.1
Statistical Features
Structural Features
o
o
o
3.1.4
SSIM
SURF Features
Edge Histogram, [7]
Contribution of Color features
Color distribution is part of security elements in ID
documents. As recommended by the ICAO [1], the use
of special Inks and Intaglio printing Process in the
background make the document more secure, uneasy to
reproduce, as illustrated in Fig. 4.
Analysis of the color printing technique is not
included in this article. This research topic will be
investigated in the second part of the project, in
collaboration with Telecom Bretagne, a member of our
consortium.
Preprocessing Step
Image acquisition is a crucial step in document fraud
analysis. The good the image quality is, the easier the
detection analysis will be. This is especially true because
details resulted by printing process may be invisible by
naked eye. This kind of analysis requires high resolution
images in order to guarantee success of the algorithm.
The preprocessing step is often used prior to descriptor
calculation in order to normalize illumination
differences provided by scanners, cameras, and other
smartphones etc., and then without suppressing printing
information.
FIG 4. Color Distribution for a genuine document
(Top) and a false document (Bottom).
3.1.5
Hybrid method
Due to the large variety of patterns that compose the
document background, it will be convenient to perform
hybrid approaches that include both statistical and
3
structural methods. This approach could be completed
by color descriptors with respect to the knowledge of
offset print properties and special inks used.
3.2
Photo Substitution
In this section, we are in presence of locally
homogenous textures which can be fully characterized
by second order statistics or hybrid spatial and spectral
approaches. As described in the section 2.2, the 4
printing techniques that are used for integrating photo
into a document can be classified in 2 classes:
o The purely random part,
o The deterministic part which is a regular,
The wold decomposition is one of possible hybrid
method we can use in this context. FIG.5 illustrates the
possible decomposition of photo printing. The
decomposition was applied on squared images which are
extracted from photo area.
FIG 6. Spectral Features analysis from FRA passport
background
Table 1 shows the result using gradient filter, SURF
algorithm from image spectrum.
Table 1 : Test results using Gradient Filter and
Surf descriptors from the image spectrum
FIG 5. Possible decomposition of photo printing
techniques.
4.
Table 2 shows the result using SURF algorithm from
image spectrum, without gradient filter
Evaluation Results
After a lot of algorithm investigations and Our first
study is based on spectral features extraction from
French and Netherland passports and evaluation of
matching procedure in order to detect clues of
falsification attempt (as photocopying).
Fig. 6 shows textured images extracted from French
passport backgrounds. Red squared images are fakes
whereas green squared images are genuine.
Table 2 : Test results using SURF algorithm from the
image Spectrum, without Gradient Filter
4
The second example is based on evaluation of twelve
samples of NLD passports. Eleven of them are genuine
documents, and one is fake document (photocopy).
5.
Conclusion
These results are promising but require much more
test samples for validation. The constitution of test
corpus has been a big step of the project. Collecting real
documents is not very easy, so we continue our
document collections in order to have a complete test
corpus. .Besides, some approaches like Multispectral
Analysis and image filtering are under investigation in
collaboration with ExoMakina, a member of our
consortium.
In the field of image acquisition, improvements are
studied by Telecom Bretagne team, in order to both
enhance image quality and increase the image
resolution.
Results corresponding to each of these subjects will
be presented in the next time.
FIG 7 . Netherland Passport, image extracted for
background Analysis
Fig 8. shows Spectral Features analysis from the
twelve samples. The second image is the fake one.
Références
FIG 8 : Spectral Features analysis Image from NLD
Passport
Table 3 shows the result using SURF algorithm from
image spectrum, without gradient filter.
Table 1 : Test Results using SURF algorithm from
the image spectrum of NLD passport
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Verification Appartus and Method, Assuretec
Systems, United States Petent, Sept, 19, 2006.
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Preliminary Results, 2012.
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Issue 1, March 2011
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