16-20 Dec 2014, IDRBT, Hyderabad

ByBiswajit Halder1, Rajkumar Darbar2, Utpal Garain3 and Abhoy Ch. Mondal1

1Dept. Of Computer Science, University Of Burdhwan, Burdhwan, W.B., India2 School of Information Technology, IIT, Kharagpur, India

3 Indian Statistical Institute, Kolkata, India

Analysis of fluorescent paper pulps for detecting Counterfeit Indian paper money

10th International Conference of Information System and Security

Counterfeiting of bank notes

Press Release -[1] “1st half of 2012 - Biannual information on euro banknote counterfeiting - on average 14.6 billion” –by European central bank on 16 July 2012[2] “In the 2011-2012 Fiscal Year, the Reserve Bank of India detected 521,155 counterfeit banknotes in India. The number of detects was 20 percent higher than 2010-2011 Fiscal Year.” – by Hindustan Times, August 26, 2012[3] "During 2013-14, the detection of counterfeit notes of Rs 1,000 and Rs 100 increased by 11.8% and 9.8 percent respectively, whereas that of Rs 500 denomination decreased by 10.3%," the central bank said in its annual report

Automatic Authentication

Required Automatic Authentication process• manual authentication of security features• time consuming process• unattractive solution

Present Drawback- Not consider paper based security - Paper quality check depend on physical contacts- Question raise on Speed and accuracy

Color optical pulp based paper security

- Embedding certain special ingredients at the

time of manufacturing- Security fibers may be metallic or photo-

chromic.- luminescent Under ultraviolet (UV) ray

Motivation

- The pattern recognisation and artificial intelligence principles.

- Ideas of feature extraction done from rice grain detection

Compare with paper pulp detection

Similarity –i) shapeii) colorii) distribution

Dissimilarity –i) other artwork involveii) factor of non pulp elements

overall approach

It’s divided into a number of stages:-(i) Detect pulps in a UV scanned banknote under

identification and verification phase(ii) Extract features from the detected pulps(iii) Train a NN classifier based training samples

that include both genuine and fake notes.(iv) Trained classifier configured as 2-class

(genuine vs. fake) problem.

Algorithm-I: PULP IDENTIFICATION

• Step 1: Acquire the currency note image (RGB) by UV light

• Step 2: Image Complement (RGB -> CMY) • Step 3: Extract cyan image • Step 4: Apply median filter • Step 5: Convert binary image by OTSU

thresholding • Step 6: Connected component labelling of

background pixels • Step 7: Compute centroid of each component

Algorithm-II: ELIMINATION OF NON-

PULP ELEMENTS

• Step 1: Around each centroid as detected at Step 7 of Al-gorithm-I, mxm sub-image is cropped from the initial RGB image

• Step 2: For each such sub-image, compute Gray Level Co-occurrence Matrix (GLCM) [17] under consideration of two adjacent pixels on four directions: 0, 45, 90, and 135 degree

• Step 3: Generate texture level four statistical features i.e. contrast, correlation, energy and homogeneity from each co-occurrence matrix [17]

• Step 4: Configure an artificial neural Network (ANN) for discriminating pulps from non-pulp particles

Detection

(a) Detected pulps after execution of Algorithm-I (b) Pulps after elimination non-pulp elements by Algorithm-II.

Flow-Chart

Features– (i) Area (f1): The number of pixels inside

pulp identified by a connected component.

(ii) Rectangular aspect ratio (f2): The ratio of the length and width of the rectangle bounding the pulp .

(iii) Pulp Aspect ratio (f3): The ratio of the lengths of the major and minor axes.

(iv) Shape factor (f4): Root means squared deviation of diameter of the pulp.

(v) Avg. HSI (f5, f6, f7)(vi) Variances of HIS (f8, f9, f10)

Training of the Classifier

Multilayer back propagation neural network (BPNN) used under following specification –

- Input, 10 node for 10 features and output, 1 node- No. of node in hidden layer computed by where I and O, input and output node

Y is no of pattern in training set- gradient descent method used for optimization

Pulp level authentication using Neural Network.

Document level authentication using

Neural Network.It No. Genuine Sample Fake Sample

  G F C F G C

1 47 2 1 45 3 2

2 49 1 0 46 1 3

3 44 3 3 47 3 0

4 48 0 2 43 2 5

Avg. 94% 3% 3% 90.50% 4.50% 5%

Behavior of the neural net in classifying pulps

(a) confusion matrix (b) ROC plot

RMSE Plot and Classification Rate

THANK YOU

I am sincerely thank the questioned document examiners of the Central Forensic Science Laboratory

(CFSL), Govt. of India for their kind help and cooperation.

Acknowledgements