FRAUDULENT SIGNATURE
DETECTION
Sayak Mukherjee, Yash Sinha, Aakriti Jain, Tamizharasi T .
Abstract – The widespread use of digital signatures has led to an increase in counterfeit digital signatures. As they are the key
to authenticating important documents for a person, forged signatures are primarily used to perpetrate fraud. Manual methods are
becoming extinct and inefficient, as technology continues to advance daily. Machine Learning is one of the most important
technologies because it is gaining importance in every discipline, including the detection of fraudulent signatures. This project
focuses primarily on Deep Learning techniques and has considered ten distinct classification algorithms to accurately determine
whether a digital signature is authentic or forged. We have conducted additional exploratory data analysis to construct our own
dataset using the signatures of VIT students and to document the model's output. Using various metrics and hyperparameters,
the results will be analysed and compared. Additionally, the analysis will include comparisons to existing models.to the fact that it
takes a novel approach and has the ability to enhance the quality of the travel experience
Index Terms— exploratory data analysis , EDA , machine learning , deep learning , CNN models , model layering
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1 INTRODUCTION
We are well aware that advancing technologies bring with them an equal number of hazards.
Cyberattacks are one such extremely extensive and vital area that requires attention. There is yet another
subdomain of digital signature fraud even here. Signatures have been a crucial form of authentication for
the longest period. With the increasing significance of digitalization in our daily lives, signatures have
shifted to a digital format. They are used in nearly every industry today, from daily transactions and
document verifications to emerging technologies such as blockchain. Therefore, it is essential to preserve
the integrity and dependability of digital signatures, preventing any form of deception or abuse. This
project's objective is to identify the most effective convolutional neural network model that we have
developed. Based on its characteristics, a dataset that can autonomously classify a digital signature as
authentic or forged.
This paper consists of two major sections. Initially, we plan to construct a model utilising digital image
processing and deep learning technologies.
(CNNs). This requires extensive research and experimentation until we arrive at a model that classifies the
vast array of signatures in our dataset with the highest degree of precision. In the second half, we will
analyse and compare the results, taking into account a variety of metrics and hyperparameter tuning in
order to obtain the best outcomes for various scenarios. In addition to precision and recall, we attempted to
maximise the true positive and true negative rates. For a complete analysis, the comparison also
incorporates existing models and studies.————————————————
Sayak Mukherjee is currently pursuing a bachelor’s degree program in Computer Science and Engineering in VIT University, India
Yash Sinha is currently pursuing a bachelor’s degree program in Computer Science and Engineering in VIT University, India
Aakriti Jain is currently pursuing a bachelor’s degree program in Computer Science and Engineering in VIT University, India
Dr. Tamizharasi T is an Assistant Professor Sr. in the School
● of Computer Science and Engineering in VIT
University,India
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is available as in the case of forgery detection. In this paper, the authors have trained a SNN
with CNN as a subnetwork. In the SNN, the input image is given the first convolutional
2 LITERATURE SURVEY
layer where different filters are applied to it. To normalize local input regions, local
Response Normalization is applied. After normalization, the image input to the next pooling
In [1], the authors propose a method of image forgery detection based on the determination
layer with The output of the first convolutional layer, local normalization and pooling layer
ofrescaling or rotation operations that have been performed on the image. The authors first
is used as aninput to the second convolutional layer having 256 different filters. In addition
calculate that rotation produces a rescaling with a factor of 1/cos x. First, they extract the
to this a dropout layer is used to avoid overfitting. To enhance the formed vector, the
edges using the Laplacian operator. The 1D discrete fourier transform of each line extracted
authors propose the use of different statistical measures such as mean and standard
in the last step is calculated. From this they obtain the frequency at which rotation induced
deviation which greatly improves the accuracy of detection. The main advantage is that it
peak occurs. The rotation angle is calculated from this. To detect forgery, the steps followed
requires little training data, no preprocessing. The disadvantage is that the output is the
are: edge detection, blocking, FT and checking if image block is rotated or rescaled. A
distance of an image from a class rather than a probability value.
histogram of peak frequencies is then made by using binning. Non zero bins are likely to be
forged components. The advantage of this algorithm is that it supports blind detection of
In [3], the authors aim to find an offline signature verification process based on a single
image rotation and rescaling without any additional features and parameters of the image.
genuine signature of a candidate. To solve the challenge of detecting fraudulent signatures
The research gap is that post-processing is done such as JPEG coding with a low-quality
using a single sample, the authors convert a single signature into a set of overlappingsub-
factor, detection becomes more difficult. Moreover, to evade rescaling/rotation detection,
images and then use feature extraction using CNN to expand the number of features. They
more sophisticated interpolation methods can be used, and image manipulations may be
also generate several forged samples to train the algorithm to identify forgery features in
done to make the rescaling traces undetectable.
different signatures. The main advantage of this method was the use of sub-images to
In [2], a Siamese Neural Network is used to make predictions when very little training data
prevent a small number of local features from influencing the outcome. The authors also
generate a color-coded saliency map over each image sub-block to make the results of
steps in this methodology can be summed as preprocessing by background clearing, noise
classification more easily interpretable by professionals such as forensic police who make
removal, centering, resizing and normalization; followed by data augmentation using
use of this application. Therefore the entire methodology can be summed up as Data
Cycle-GAN and then verification using CapsNET. The main disadvantage of this
cleaning and noise removal, followed by data augmentation and classification. This is
methodology is that it gave comparatively poor results on the writer independent approach
followed by rotationof the image sub-blocks by predefined angles clockwise and repetition
that the writer dependent approach.
of the entire process. This rotation helps eliminate rotation-dependent features of
handwriting such a slant inhandwriting and helps increase the accuracy of the classification
This paper [5], proposes the use of convolution neural networks( CNN) supported by
task. The only disadvantage of this method was its relatively poor performance in
additional feature extraction methods to classify forged offline signatures. Offline
classifying Chinese signatures due tothe complex structure of Chinese characters.
signatures are harder to verify due to external criteria like age, health condition etc of the
person who is signing. In this system, the input layer to CNN contains the image data. The
In [4], the authors have proposed a deep learning-based data augmentation method to solve
image is passed to the convo layer which performs feature extraction by calculating dot
the problem of having a limited number of forged and genuine signatures. This method is
product between repetitive fields. The output of this layer, a single integer of output volume
based on the cycle-GAN deep learning method. In this method, a GAN approach is taken to
is sent to the pooling layer which is used to reduce the spatial volume of the image after
learn the mapping between input and target images that are similar to each other using a
convolution. Followed by this is a fully connected layer and softmax layer before the final
combination of supervised regressing and adversarial loss. Another advantage of this
output layer. The authors have also deployed this model using the Django framework for
method is its overcoming adversarial attacks while training a CNN by using CapsNET
ease of use. The main advantage of this method is the use of transfer learning by using
verifier to model spatial relations accurately. Adversarial attack is the manipulation of
the Alex NET CNNand GoogLeNet as a base pretrained model. The main disadvantage of
Image vectors in a specific manner to intentionally cause the model to misclassify the
this method is that the authors have not used any custom feature extraction methods as
instance. Using CapsNET vectors approach reduces this threat considerably.Therefore the
compared to the previous papers so the accuracy scores are lesser.
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correspondingstatic sample. The neural network model can learn how online information
The approach in [6] is based on CNN (Convolutional Neural Network) which is trained in
is transformed into offline information so that the synthetic signatures can be used to
a supervised manner so that it can learn the representation of the offline manuscripts using
improve the
the input from the online representation. The model is expected to predict the
recognition rates. The images are inverted so that the white background corresponds to
reason why color based classification is not dealt with in the hyperspectral analysis based
pixel intensity 0. The input is also normalized so that the model can predict the data more
approach. In [8], many different types of algorithms were used such as Support Vector
accurately. Each of the pixels is divided by the standard deviation of all pixel intensities.
Machines (SVM), Multi Layer Perceptron(MLP) and Convolutional Neural Network (CNN)
The leaky rectified linear units are used as the activation function for all the convolutional
models. The accuracy and time will be compared to know the best model for the digit
networks. The model was trained using the Adam optimiser to minimize the minimum
recognition.The images in the input data are reshaped to present it in 2D images i.e. (28, 28,
squared error loss for 100 epochs. Two different experiments are done for the result. The
1). The pixelvalues have been normalized, so that the input features will range between 0 to
first experiment is to measure the similarity between synthetic and real signatures, two
1. For the MLP different activation functions were used, dense layers of different
different protocols are used to train the system i.e. mono-session and multi-session. The
specification and dropout layers were used. Neural network with 4 hidden layers and an
second experiment tests whether synthetically increasing the enrolment dataset leads to
output layer with 10 units is used. To introduce non linearity to the model Rectified Linear
better recognition performance. The results are compared in the terms of Equal Error Rate
Unit activationfunction was used apart from the dense layer. For the CNN different layers
(EER) and Detection Error Tradeoff (DET). In [7], a Convolutional Neural Network is used
with the softmax layer. Leaky Rectified Linear Unit activation function has been used to
to develop the model with many different layers and neurons in order to predict if the given
increase the non-linearity. The dropout layer is there to drop some of the neurons which are
handwritten signature is forged or not. They have experimented with two different filter
chosen randomly so that the model can be simplified.. The flattening layer is there to
sizes
generate a column matrix from the 2 dimensional matrix. The last layer functions return
i.e. 3x3 to 5x5 in our CNN architectures. So for each of the filter sizes, three different sets of
probability distribution over all the 10 classes and the class with the maximum probability
convolutional layers are used, i.e. 2, 4 and 6 convolutional layers with alternating Rectified
is the output. In [9], the handwritten signatures are collected and some unique features are
Linear Unit and varying filters. Leaky Rectified Linear Unit is used as the activation
extracted to create a knowledge base of each and every individual. A standard database of
function.Each of the CNN architecture is separately trained and tested for blue and black
signatures for every individual is needed for evaluating performance of the signature
inks.Appropriate selection of architecture and training parameters has been done to aid the
verification system and also for comparing the result obtained using other techniques. There
Convolutional Neural Network. The CNN classifies the ink pixels to detect ink mismatch in
are many preprocessing techniques that were used, first the image was converted from RGB
an input hyperspectral image. The ink pixels in the hyperspectral image are labeled for the
to Grayscale. The Digital Image Processing (DIP) needs to be converted to grayscale image.
sake of visualization of any potential ink mismatch in the document. The blue and black
This decreases the computational complexity of DIP drastically and helps in running
inks can be distinguished by visual inspection for using color image processing, that is the
the
image processing algorithms in a very smooth way. For Noise Removal, a known form of
made by the authors with one person making the originals and the other person making the
noise is introduced in very small quantities, this ensures that the threshold of noise level
forgeries. They have used Tensorflow for the model backend. They split the dataset in an
inthe image increases and hence is easily detectable by the denoising filters. The grayscale
8:2 ratio for training and testing which obtained them an accuracy of 98%. The future
image format is converted into bitmap where image file format is used to store digital
enhancements suggested by the authors would be to do comprehensive research on loss
images.Raster images are in general also referred to as a bitmap. The system must be able
functions and derive a custom loss function which would predict the user to which the
to maintainhigh performance regardless of the size. It is important that the system must be
signature belongs to, and whether it is a forgery or not. In [10], the researchers have
insensitive enough for the correction in the signature image. The image is rescaled to 256 x
prepared synthetic signature databases that allow the integration of a large number of users
256 in this case. They have used the Keras Python Library for implementing the
under a wide variety of situations. Synthetic databases can be useful until an equivalent real
Convolutional Neural Network. The model that was derived has been verified using
database is developed. They have taken a corpus of datasets from the internet. The majority
accuracy and loss metrics tosee how well the model has fit the data. The image goes through
of the databases that they are using have been collected in laboratories. They have the digital
3 convolutions and max pooling layers which are in an alternating fashion. The dataset was
signatures of different languages. There are different methods of the verification system
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such as Western Signatures Verification Systems, Chinese Signature Verification, Japanese
identification stage. They achieved an accuracy of 97.70% in identification. An analysis has
Signature Verification, Arabic Signature Verification and Signature Verification in Indic
been carried out of the weaknesses and strengths of the most common publicly available
Languages. The multiscript signature verification system used in several different scripts
offline and online signature databases. ASV systems have traditionally been developed to
can be verified by using the same ASV. Signatures are typically executed in the original
cope with Western based signatures.
script and can be aggregated to a common database. The authors presented two offline
signature verification experiments: first without initial script identification and second with
The images in [11] are stored in a directory which is compatible with the Python Keras
initial script identification for English, Hindi and Bengali. The system developed for this
Library. The researchers preprocessed the images by resizing them to a resolution of 512
study utilized chain code features and signature gradients along with support vector based
X
classification. The study consisted of eight offline public signature datasets, available in five
512. The jpeg images, if any, had their content decoded to RGB grids of pixels with channels
different scripts. The authors showed that similar results are obtained when merging the
and were sent in RGB form to the CNN model.The CNN was implemented in python using
datasets or keeping them singular, thereby implying multiscript verification as a
the Keras library with the TensorFlow backend and consequently the model learns
generalized problem. High accuracies 99.41%, 98.45%, 97.75% were obtained by studying
the
different techniques. The authors applied a foreground and background technique for the
patterns associated with the signatures. The image passes through a continuous sequence
reach the output layer where the sigmoid activation function is applied for one last time and
of convolution and max pooling layers. A predefined number of feature maps are fed into
this becomes the output of the neural network. This is followed by the phase of back
a maxpooling layer, which in turn produce feature maps from the feature maps generated
propagation which is used for calculating the errors. The number of output layers is 2.
byprevious layers. This process continues till the fourth max pooling layer. After this the
Gradient descent algorithm was used for minimization of errors.
feature map is flattened and directed towards the fully connected layers wherein after
multiple iterations of forward and backpropagation a trained model is obtained which can
The researchers in [13] extensively reviewed the Learned iterative shrinkage and
be used to make predictions. The Dataset used by the researchers is a collection of 10
Thresholding Algorithm(LISTA). Each iteration of the ISTA constitutes one linear
signatures,with 5 real and 5 forged signatures per person. In addition, they had a kaggle
operationfollowed by a nonlinear soft-thresholding operation, which mimics the
dataset with 30 real signatures
ReLU activationfunction. One can come up with a deep network by mapping each
The software the researchers in [12] used to develop the system was the WingWare Python
iteration to a network layerand stacking the layers together, which is equivalent to
IDE and the Anaconda IDE in addition to the required library modules. In the data
executing an ISTA iteration multiple Times. After unrolling the ISTA into a network, the
acquisitionphase, the researchers used the signature dataset collected from Sigcomp 2011
network is trained using training samplesthrough backpropagation.
explored other
unrolling
They
database. This database comprises 239 genuine signatures and 123 forged signatures. Of the
further
239 genuine signatures, 219 were directed towards training and 20 went towards the testing
likeDURR(dynamically unfolding recurrent restorer) which is capable of
algorithms
set. Similarly, of the 123 forged signatures, 103 were used in training and the rest were kept
providing high qualityreconstruction of images that possess much sharper details.
for testing purposes. In the image processing phase, The researchers concluded that there
Further, it generalizes when theimage is highly degraded.
was no need for image pre-processing and feature selection techniques. Only data
augmentation is needed. In the training stage, an image is retrieved from the signature
Traditional data augmentation approaches discussed in this paper [14] are based on a mix
database and sent towards the neural network. A 6-layer multi-perceptron(MLP) neural
of affine picture transformations and color change, are quick and simple to apply, and have
network was used. The image pixelsare first fed to the input layer. The size of the first
beenshown to be effective in expanding the training set. Even though such approaches have
hidden layer was set to 13. The size of the second hidden layer was 7, The third hidden layer
been effectively implemented in a variety of industries, they are subject to adversarial
was 5 and the fourth hidden layer was 4.These nodes are further sent to the first hidden
attacks. Furthermore, they do not provide any new visual elements to the pictures that
layer. The hidden layers use the sigmoid activation function. The input pixels eventually
might
considerably increase the algorithm's learning skills as well as the networks' generalization
findings that are novel to the human eye as well. GANs may synthesize pictures from
abilities. Methods based on deep learning models, on the other hand, frequently provide
scratch in any category, and combining GANs with other approaches can provide gratifying
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results (for example, combining random erasing with image inpainting through GAN).
adversarial networks(GANs). One other crucial step in preprocessing during CNN training
However, GANs have limitations: computation time is long, there are evident difficulties
is patch selection. A number of algorithms with the concept of thresholding and color
with counting,a lack of perspective, and difficulty coordinating global structure. The texture
deconvolution were used to extract pixels from the regions of interest. The main post-
and color transfer issues are intertwined with the style transfer issue. Classic style transfer
processing method applied image waspatch aggregation. A patch aggregation approach
methods are comparable to traditional texture transfer methods, but they include a color
takes into consideration the characteristics and the labels of all the patches extracted by the
transfer as well. Transfer learning, which, like GANs, employs deep learning models, has
CNN to predict the final image label.
recently gained in prominence. An algorithm developed for creating artistic-style
photographs allows the information and style of the image to be separated and recombined.
In [16], The problems and inconsistencies related to manual static signatures are specified.
When the substance of one image is combined with the style of another, the result is an
These inconsistencies may arise due to the ever-changing behavioral metric of the person in
entirely new image that combines the characteristics of both photos. However, they too have
consideration and may lead to inconsistent data. Hence, verification and authentication for
limitations and they are highly dependent on the structure of the image.
thesignature may take a long time as the errors are comparatively higher. Thus, Inconsistent
signature leads to higher false rejection rates for an individual who did not sign in a
This analysis in [15] focuses on the influence of various pre- and post-processing approaches
consistent way. The proposed implementation is - an image goes through a series of
used in deep learning frameworks to deal with the extremely complex patterns found in
convolution and max pooling layers which are in an alternating fashion. When the image
histology pictures. Many of the techniques discussed in the pair, particularly the post-
goes through convolution process, a predefined number of feature maps are created which
processing approaches, were not confined to histological image analysis but were used
arefed into a max pooling layer, which creates pooled feature maps from the feature maps
in practically any discipline of image analysis. In the scope of pre-processing, the algorithm
received from the convolution layer which is before it. This pooled feature map is sent into
the researchers analyzed was the stain normalization algorithm. The stain normalization
the next convolution layer and this process continues until we reach the fourth max pooling
process standardizes the stain color appearance of a source image with respect to a reference
layer. The pooled feature map from the last max pooling layer is flattened and sent into
image. They range from global color normalization to color transfer using generative
the
fully connected layers. After several rounds of forward and backward propagation, the
of the second hidden layer was7, The third hidden layer was 5 and the fourth hidden layer
modelis trained and a prediction can now be made.
was 4. These nodes are further sent to the first hidden layer. The hidden layers use the
sigmoid activation function. The inputpixels eventually reach the output layer where the
In [17], the authors propose an off-line handwritten signature verification method based on
sigmoid activation function is applied for one last time and this becomes the output of the
an explainable deep learning method (deep convolutional neural network, DCNN) and
neural network. This is followed by the phase of back propagation which is used for
unique local feature extraction approach. Open-source dataset, Document Analysis and
calculating the errors. The number of output layers is 2.Gradient descent algorithm was
Recognition (ICDAR) 2011 SigComp has been used to train the system and verify a
used for minimization of errors.
questioned signature as genuine or a forgery. Testing and Training dataset has been kept
different to test the out-of-sample accuracy. In the data acquisition phase, the researchers
The paper numbered [18] proposes a method for the pre-processing of signatures to make
used the signature dataset collected from Sigcomp 2011 database. This database comprises
verification simple. It also proposed a novel method for signature recognition and signature
239 genuine signatures and 123 forged signatures. Of the 239 genuine signatures, 219 were
forgery detection with verification using Convolution Neural Network (CNN), Crest-
directed towards training and 20 went towards the testing set. Similarly, of the 123 forged
Trough method and SURF algorithm & Harris corner detection algorithm. After converting
signatures, 103 were used in training and the rest were kept for testing purposes. In the
all the training examples into the desired format, Pre-processing Algorithms on CC were
image processing phase, the researchers concluded that there was no need for image pre-
used. Signature verification system typically needs the answer of 5 sub-issues: data
processing and feature selection techniques. Only data augmentation is needed. In the
retrieval, pre-processing, feature extraction, identification method, and performance
training stage, an image is retrieved from the signature database and sent towards the
analysis. These were generally classified and applied to all the images. When the image is
neural network. A 6-layer multi-perceptron (MLP) neural network was used. The image
finally classified into one among the present classes of the topics. The henceforward system
pixels are first fed to the input layer. The size of the first hidden layer was set to 13. The size
proposes atechnique for the detection of the forgery within the same by checking the chosen
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options against the on the market set of pictures for the signature and produces a binary
The authors of paper [19] have proposed a new descreening technique for Frequency
result if it’s cast or not. Here two Algorithms are used for Forgery Detection: Harris
Domainfilters. It is based on the idea that the original screen pattern is easy to detect in the
Algorithm and Surf Algorithm. The proposed system attains an accuracy of 85-89% for
Fourier domain. So, the authors propose a new filter and how to create it in order to tackle
forgery detection and 90-94% for signature recognition.
this issue.
The descreen algorithm proposed in this paper works in the frequency domain. The basic
machine classifier to classify the images based on the features extract by the CNN. The
idea is that the screen pattern can be detected and properly removed because it is
datasets used in this paper are the LG2200 dataset, it contains 116,564 images from 676
intrinsically regular and periodic. In some sense, the screen signal can be associated with
people, and CASIA-Iris-Thousand dataset which contains 20,000 images from 1000 people.
some kinds of periodical noise. The main differences are that such "noisy" values cannot be
70% of the data is used for training and 30% for classification. The different
completely removed because it also carries out the original signal. Preserving, of course, the
softwares/libraries used in this paper are USIT v2.2 for image segmentation and
right amountof low frequency components it is possible to properly search the anomalous
normalization, PyTorch for training the CNN and LIBSVM for the SVM classifier.
peaks and deletethem in a suitable way. These peaks are properly characterized by some
peaks located at a given distance from the DC component. In such a manner the main low
In the first step, image segmentation is performed by using one of the most commonly used
pass component is preserved and the final recovered image is not too blurry.
circle detectors, the integro differential operator. This operator detects circular edges by
iteratively searching based on the parameters (x0, y0, r). This isolates the iris region of
In [20], the authors have applied frequency domain filters to generate enhanced images.
theeye from eyelashes, eyelids and sclera. Due to contraction and dilation of the pupil, the
Simulation outputs result in noise reduction, contrast enhancement, smoothening and
size of the iris can vary from image to image, this variation is accounted for in the
sharpening of the enhanced image. In frequency domain methods, the image is first
normalization step. The segmented iris revision is mapped to a fixed rectangular region
transferred into the frequency domain. All the enhancement operations are performed on
using the rubber sheet model. This is done by mapping the iris region from raw cartesian
the Fourier transform of the image. The procedures required to enhance an image using
coordinates to dimensionless polar coordinates. Normalization has the additional benefit of
frequency domain technique are: Transform the input image into the Fourier domain,
removing the rotations of the eye.
Multiply the Fourier transformed image by a filter and finally, Take the inverse Fourier
For feature extraction, the normalized images of the iris are fed into the CNN feature
transform of the image to get the resulting enhanced image.
extraction module, five CNNs (AlexNet, VGG, Google Inception, ResNet and DenseNet) are
used to extract features from the normalized iris images. Different layers in the CNN models
The steps employed by the authors, in [21], in training the iris classifier are preprocessing
the image differently, with later layers encoding finer and more abstract information
the dataset which includes performing image segmentation and normalization on the
and
dataset, using CNN to extract features from the images and finally using a support vector
earlier layers retaining coarser information. The feature extracted vectors from the CNNs
achieved the achieved accuracy of 98% and 98.2% on LG2200 and CASIA-Iris-Thousand
are fed into the SVM classifier, a multi-class SVM is used with “one against all” strategy.
dataset. VGG achieved the lowest accuracy of 92.7% and 93.1% due to its simple
architecture. Advantages: The authors achieved higher accuracy than traditional methods.
The performance of the system is reported using the Recognition Rate, which is calculated
Itis easier to use CNN based classifiers in large scale applications. CNN based classifiers are
as the proportion of correctly classified samples at a predefined False Acceptance Rate of
more secure for use as biometric locks. Disadvantages: The computational cost of training
0.1%. Gabor phase quadrant feature descriptor is used as the baseline feature descriptor, its
CNNs is much higher than traditional methods. In real time applications, traditional
accuracy is 91.1% on LG2200 and 90.7% on the CASIA-Iris-Thousand datasets.
methods are
faster than CNN based methods. Future works: Other neural network
architectures, such as Deep Belief Network (DBN), Stacked Auto Encoder (SAE) etc, can be
Authors found that among all five CNNs, DenseNet achieved the highest accuracy of 98.7%
used for feature extraction in iris recognition.
on LG2200 dataset and 98.8% on CASIA-Iris-Thousand dataset. ResNet and Inception
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the S vector in this case will be able to detect forgery but is at risk of misclassifying genuine
The authors in the [22], constructed a dataset by, first, gathering 20 signatures from 20
signatures, using subject’s signature and other’s signatures, using all original signatures and
different individuals and then after a month another batch of 20 signatures were gathered
using subject’s original and forged for the subject. The output from the CNN is used as the
from 10 individuals. Each signature was given to a forger to imitate five signatures. The
input of an AE. The scheme used in the paper is a five layer AE that is trained using the
signatures from the dataset are preprocessed by normalizing the alignment, size and length
samedata as input and output. To test the AE, a new signature is given to it and the resulting
of the signature before feature extraction. First each signature is aligned so that the starting
vectoris compared with the vector for the original signature, if the difference between them
point is set to (0, 0, 0, 0, 0), then an equation is used to normalize the alignment of the
is less than a predefined threshold then the signature is accepted as a valid one.
signatures. Finally, length and width of the signatures are normalized.
The results of the experiments show an increase of 13.7% percent accuracy when CNN based
CNNs have two parts, an automatic feature extractor and a trainable classifier. In this paper
feature extraction is compared with other methods. Advantages: CNN trained with forged
only the feature extractor is used. In the CNN feature extractor, the convolution layer uses
andgenuine signatures could generate an S vector that achieved better results than other
thenormalized signature as the input value. The output of the CNN is a vector called the
methods.
S-vector. The training data for the CNN is of four types: the genuine and forged signatures,
The trained model can be used in many different applications, without the need for
comparedwith AlexNet, ResNet and DenseNet, FSSNET, SAGP and PRAN algorithms.
retraining it. Disadvantages: The computational cost of training CNNs is much higher than
traditional methods.
Experiments on the Indian Pines data set resulted in AML being the best algorithm but all
The signatures that were taken on a smartphone and hence the method can’t be used with
of the algorithms were 1% to 8% in terms of the performance metrics of each other. On the
images of signatures on paper. Future works: Identifying forged signatures drawn on paper.
Pavia University data set, the AML algorithm was closely followed by the PRAN algorithm
and on the Salians dataset, again the ALM algorithm obtained the best results. Advantages:
In [23], First the original hyperspectral image is reduced by the use of PCA algorithm, this
The proposed algorithm performs better than all other test algorithms in all the tested data
algorithm reduces the number of dimensions required to represent the image by only
sets. Disadvantages: The proposed algorithm is more complex than other methods due to
keeping the useful features of the image. A probability weight distribution mechanism
the required multistep processing. Future works: Test the algorithm on other datasets and
called the attention mechanism is used to improve the quality of hidden layer features.
try to reduce the complexity of the involved steps.
Probability weight distribution mechanism calculates the features of remote sensing images
input at different times and pays attention to the features of target recognition. Next a
multiscale convolution kernel is used to alleviate the loss of features due to the use of
In paper [24], Point based methods have efficient memory requirements but they have
traditional convolution kernels. This multiscale kernel mines for features from multiple
irregular memory access patterns that introduce computation overheads. In point based
scales simultaneously. Bi-LSTM is then used to improve the correlation between deep
methods, neighboring points are not laid out contiguous in memory and since the 3D points
features. It also avoids the problem of gradient explosion and gradient disappearance. Bi-
are scattered in R3, the neighbors of a point need to be explicitly calculated and can’t be
LSTM uses twoLSTM, one for forward and one for backward to strengthen the semantic
directly indexed. The combination of these two factors cost 30% to 50% of total runtime.
information between deep features.
Voxel based models have regular memory access and good memory locality but they
Three data sets were used in this paper, Indian Pines (145 * 145 in size and reduced to 100
require high resolution in order to not lose information. On an average GPU with 12 GB of
dimensions with PCA), Pavia University scene (610 * 340 in size and reduced to 50
memory, the largest possible resolution is 64 which will lead to 42% of information loss
dimensions with PCA) and Salinas (which is 512 * 217 in size and reduced to 29 dimensions
and to retain
with PCA). Overall accuracy, average accuracy and the kappa coefficient are used as the
>90% information, at least 82 GB of memory is required, which is around 7 GPUs. This is
performance metrics to evaluate the model. The experiment was repeated 10 times and the
why voxel based models are prohibitive for deployment.
highest value was taken each time. The proposed AML algorithm in this paper was
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The model proposed by the author combines the advantages of point based and voxel based
methods. It does that by disentangling fine grained feature transformation and the coarse
grained neighbor aggregation so that each branch can be implemented efficiently and
effectively. Points are transformed into low resolution voxel grids, voxel based convolutions
are then performed, finally the result is devoxelised to get the points back.
The method was tested on following tasks: object part segmentation, indoor scene
segmentation and 3D object detection. It was found that the proposed methodology is
superior in all the tested tasks with lower latency and GPU memory consumption.
Advantages: Better results with lower memory latency and consumption. Disadvantages:
Need more testing to further prove the results. Future works: More testing on more
sophisticated tasks to get a better idea of advantages and disadvantages of the proposed
model.
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the most pivotal steps in any Machine Learning
model as the performance of the model greatly
3 CUSTOM DATASET
depends on how well processed the dataset is. For
In this final part of the project we have conducted
an exploratory data analysis and created a dataset
from VIT students whose signatures we have
acquired.
this purpose, we plan to have the following image
processing pipeline:
The raw image shall be read from the dataset
and converted to a grayscale image forapplying
morphological transformations.
These are few samples of the custom dataset that
we have created.
The rest of the raw dataset can be viewed from
the link given below:
https://drive.google.com/drive/folders/16tyIeylDF
19E4OjiJzwoUMrFGfQPHoq5?usp=sharing
1.
Noise removal can be achieved by
incorporating morphological
transformations.
2.
The image is then converted to a
binary image to have just pixel
values in the set{0,1} where 0 stands
for a black pixel and 1 stands for a
white one. (binarization)
4 IMAGE ACQUISITION
3.
The resultant pepper noise is removed
using a median filter. (median)
4.
The image is then inverted and
forwarded to the ML model.
The first step for any deep learning model is to
gather data and a lot of it. To do the same, we
researched datasets which were already prepared
and annotated, but we couldn’t find any such
6 PROPOSED METHOD
relevant datasets for our model. Hence, we
acquired 3 datasets in raw format which had to be
We have considered 10 deep learning models
processed and annotated later on.
having separate characteristics to train and
compare their output on our custom dataset
The primary purpose to use 3 datasets was to
have the required variety in our training data for
the model. These 3 datasets comprise of the
They are as follows :
VGG16
following features:
Dataset 1 has signatures in English without much
background noise.
VGG-16 is a deep convolutional neural network
architecture that was developed by the Visual
Dataset 2 has signatures in English with
background noise.
Geometry Group at the University of Oxford. It
Dataset 3 has signatures in Hindi and Bengali.
Large-Scale
was designed to participate in the ImageNet
Visual
Recognition
Challenge
(ILSVRC) in 2014, and it achieved outstanding
This gave us sufficient variety required to
performance
achieve a good enough accuracy and enable the
architecture consists of 16 layers, including 13
model to predict if a particular signature is genuine
convolutional layers, 5 maxpooling layers, and 3
or not in any language.
fully connected layers. The convolutional layers
on
this
task.
The
VGG-16
have small 3x3 filters, and they are arranged in a
way that makes the network deeper and narrower
5 IMAGE ENHANCEMENT
than previous architectures. VGG-16 has around
Pre-processing the data is considered to be one of
138 million parameters, making it a very large
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network that requires significant computational
layers
resources to train. However, it has proven to be
architecture of ResNet-50 is based on a series of
very effective at tasks such as image classification,
residual blocks, each of which contains two or
object detection, and image segmentation. VGG-
more
16 has been widely used as a pre-trained model
connection that bypasses the convolutional layers.
for transfer learning, where it is finetuned on new
The shortcut connections enable the information
datasets to perform specific tasks. It has also
to flow directly from the input of one block to the
served as a benchmark for comparing the
output of another block, which helps to alleviate
performance of newer architectures, such as
the vanishing gradient problem that can occur in
ResNet and Inception.
deep networks. This allows ResNet-50 to be much
VGG19
deeper than previous architectures, while still
VGG-19 is a deeper version of the VGG-16
maintaining good performance. ResNet-50 has
architecture, also developed by the Visual
been pre-trained on large-scale datasets, such as
Geometry Group at the University of Oxford. It
ImageNet, and has been shown to achieve state-
was introduced in the same paper as VGG16, and
of-the-art performance on a wide range of image
it consists of 19 layers, including 16 convolutional
classification tasks. It has also been used for
layers, 5 max-pooling layers, and 3 fully
transfer learning in a variety of computer vision
connected layers. The architecture of VGG-19 is
applications,
including
similar to VGG-16, with the only difference being
segmentation,
and
the number of convolutional layers. VGG-19 has
approximately 23 million parameters, ResNet-50
three additional convolutional layers compared to
is significantly smaller than VGG-16 and VGG-19,
VGG-16, which makes it even deeper and more
making it more computationally efficient.
complex. Similar to VGG-16, VGG-19 has 3x3
RESNET101
filters in all of its convolutional layers, which
ResNet-101 is a deeper version of the ResNet
makes
in
architecture, introduced by Microsoft Research in
capturing spatial features in images. However,
2016. Like ResNet-50, ResNet-101 is based on a
due to its increased depth, VGG-19 requires even
series of residual blocks, each of which contains
more computational resources to train and has
two or more convolutional layers and a shortcut
approximately 143 million parameters. VGG-19
connection. The main difference between ResNet-
has also been widely used as a pre-trained model
50 and ResNet-101 is the number of layers.
for transfer learning, especially in tasks such as
ResNet101 consists of 101 layers, including 100
image classification and object detection. Despite
convolutional layers and 1 fully connected layer.
its
This deeper architecture allows ResNet-101 to
the
architecture highly
complexity,
excellent
VGG-19
performance
has
demonstrated
fully
convolutional
connected
layers
image
and
object
layer.
a
The
shortcut
detection,
captioning.
With
capture more complex patterns and features in
classification benchmarks, and it remains a
images, and it has been shown to achieve even
popular
better performance than ResNet-50 on various
for
many
various
1
image
choice
on
effective
and
deep
learning
applications.
image classification tasks. ResNet-101 has been
RESNET50
pre-trained on large-scale datasets such as
ResNet-50 is a deep convolutional neural network
ImageNet and has been used as a pre-trained
architecture that was introduced by Microsoft
model for transfer learning in various computer
Research in 2015. The name ResNet comes from
vision applications, including object detection,
"residual network, " which refers to the use of
segmentation,
residual connections between layers to enable the
approximately 44 million parameters, ResNet-101
training of much deeper networks. ResNet-50
is larger and more computationally expensive
consists of 50 layers, including 49 convolutional
than ResNet-50, but it can provide even better
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and
image
captioning.
With
performance for more challenging tasks. Overall,
image classification and object recognition tasks.
ResNet-101
learning
Inception-v3 has also introduced several new
architecture that has demonstrated outstanding
techniques to improve the performance of the
performance on many image recognition and
network. For example, it uses factorized 7x7
computer vision tasks. Its ability to capture and
convolutions,
represent complex features has made it a popular
convolution into two smaller convolutions,
choice for a variety of deep learning applications.
reducing
RESNET152
improving
ResNet-152 is a deeper version of the ResNet
normalization and RMSprop optimization to
architecture,
Microsoft
improve training stability and speed. Inception-v3
Research in 2016. Like ResNet-50 and ResNet-101,
has been pre-trained on large-scale datasets such
ResNet-152 is based on residual blocks and
as ImageNet and has been shown to achieve state-
utilizes shortcut connections to enable the training
of-the-art
of very deep neural networks. The main difference
classification tasks. It has also been used for
between ResNet-152 and the other ResNet
transfer learning in a variety of computer vision
architectures is its depth. ResNet-152 consists of
applications,
152 layers, including 151 convolutional layers and
segmentation, and image captioning. Overall,
1 fully connected layer. This makes it one of the
Inception-v3
deepest neural networks that have been proposed
architecture that has demonstrated outstanding
for computer vision tasks. The additional layers in
performance on many computer vision tasks. Its
ResNet-152 enable it to capture even more
ability to capture both local features and global
complex features and patterns than ResNet-101,
patterns,
making it suitable for more challenging image
computational resources, has made it a popular
classification and object recognition tasks. It has
choice for a wide range of deep learning
been
applications.
is
a
also
shown
performance
powerful
introduced
to
on
achieve
various
deep
by
state-of-the-art
image
recognition
which
the
break
number
efficiency.
of
also
on
including
along
a
uses
various
object
powerful
with
its
a
7x7
parameters
It
performance
is
down
deep
efficient
and
batch
image
detection,
learning
use
of
INCEPTIONRESNETV2
benchmarks, including the ImageNet Large Scale
Inception-ResNet-v2 combines the multi-scale
Visual Recognition Challenge (ILSVRC). ResNet-
feature extraction of Inception with the residual
152 has also been widely used as a pre-trained
connections of ResNet. It consists of a series of
model for transfer learning in computer vision
Inception modules, each of which includes
applications
such
as
multiple parallel convolutional operations at
segmentation,
and
image
object
detection,
captioning.
With
different scales, along with residual connections
approximately 60 million parameters, ResNet-152
that bypass the convolutional layers. This
is larger and more computationally expensive
combination of techniques helps to improve the
than ResNet-50 and ResNet-101. However, its
accuracy of the network while maintaining its
ability to capture complex features and patterns
computational efficiency. Inception-ResNet-v2
makes it a powerful deep learning architecture for
also introduces new techniques to improve the
a wide range of computer vision tasks.
performance of the network, such as "dynamic
INCEPTIONV3
scaling" of the input images, which allows the
The Inception-v3 architecture consists of a series
network to adapt to images of different sizes. It
of Inception modules, each of which performs
also uses "label smoothing" during training, which
multiple parallel convolutional operations at
helps to reduce overfitting and improve the
different scales. The Inception module is designed
generalization ability of the network. Inception-
to capture both local features and global patterns
ResNet-v2 has been pre-trained on large-scale
in images, which makes it highly effective in
datasets such as ImageNet and has been shown to
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achieve state-of-the-art performance on various
MOBILENETV2
image classification and object recognition tasks.
One of the key improvements of MobileNetV2 is
It has also been used for transfer learning in a
the use of a new block structure called the
variety of computer vision applications, including
"inverted residual with linear bottleneck". This
object
image
block structure consists of a linear bottleneck
captioning. Overall, Inception-ResNet-v2 is a
layer, followed by a depthwise convolution layer,
powerful
that
and then a linear projection layer. This structure
combines the strengths of Inception and ResNet.
allows MobileNetV2 to capture both low-level
Its ability to capture both local features and global
and high-level features of the data, while still
patterns,
maintaining a high degree of computational
detection,
segmentation,
deep
learning
along
with
its
and
architecture
efficient
use
of
computational resources, has made it a popular
efficiency.
choice for a wide range of deep learning
activation function
applications.
convolutional activation", which reduces the
MOBILENET
computational cost of the pointwise convolution
MobileNet achieves this by using depthwise
by removing the need for an additional non-linear
separable convolutions, which consist of a
activation function. In addition, MobileNetV2
depthwise convolution followed by a pointwise
introduces several new techniques to further
convolution. The depthwise convolution applies a
improve its performance, including "shortcut
single filter to each input channel separately,
connections" between the bottleneck layers, which
while the pointwise convolution applies a 1x1
helps to reduce the vanishing gradient problem
convolution to combine the output of the
and improve the training stability of the network.
depthwise convolution. This approach reduces
It also uses "channel pruning" to reduce the
the number of parameters and computations
number of channels in the network without
required, while still allowing the network to
significantly impacting its accuracy. Overall,
capture complex patterns in the data. MobileNet
MobileNetV2 is a powerful deep learning
also uses "bottleneck" layers to further reduce the
architecture that has further improved the
computational complexity of the network. These
efficiency and performance of the MobileNet
layers use 1x1 convolutions to reduce the number
architecture. Its ability to capture both low-level
of input channels before applying the depthwise
and high-level features of the data, while still
separable
1x1
maintaining a high degree of computational
output
efficiency, makes it well-suited for a wide range of
convolution,
convolutions
again
to
and
then
expand
use
the
MobileNetV2
also
uses
a
new
called "linear pointwise
channels. MobileNet has been pre-trained on
mobile and embedded vision applications.
large-scale datasets such as ImageNet and has
NASNETLARGE
been
state-of-the-art
The NasNetLarge architecture consists of a series
performance on various image classification tasks.
of "normal" and "reduction" cells, each of which
It has also been used for transfer learning in a
contains multiple convolutional layers with
variety of computer vision applications, including
different filter sizes and dilation rates. The normal
object
facial
cells use skip connections to allow information to
recognition. Overall, MobileNet is a powerful
flow directly from earlier layers to later layers,
deep
been
while the reduction cells use max pooling to
optimized for mobile and embedded devices. Its
reduce the spatial size of the feature maps.
ability to achieve high accuracy with fewer
NasNetLarge
parameters and computational resources makes it
regularization" during training, which encourages
well-suited for a wide range of mobile and
diversity in the architectures produced by the
embedded vision applications.
search algorithm. This helps to prevent the
shown
to
detection,
learning
achieve
segmentation,
architecture
that
and
has
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also
includes
"path-level
algorithm from converging to a single, overfit
architecture and encourages the exploration of a
5
VGG16
wide range of architectures. NasNetLarge has
been pre-trained on large-scale datasets such as
ImageNet and has been shown to achieve state-ofthe-art
performance
on
various
image
classification tasks. It has also been used for
transfer learning in a variety of computer vision
applications,
including
object
detection,
VGG19
segmentation, and facial recognition. Overall,
NasNetLarge is a powerful deep learning
architecture that demonstrates the potential of
neural architecture search for automating the
process of architecture design. Its ability to
capture complex patterns in the data, along with
its high degree of computational efficiency, makes
it well-suited for a wide range of computer vision
RESNET50
applications.
RESNET101
RESNET152
7 RESULTS AND DISCUSSION
The confusion matrix and the ROC curve has been
presented below :
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INCEPTIONV3
Using mobile net we try to distinguish fraud
signatures from real signatures
Identified fraud signature
INCEPTIONRESNETV2
Identified real signature
MOBILENET
MOBILE NET V2
PLOTTING THE MODEL VS MODEL ACCRACY
6 CONCLUSIONS
In this project, by utilizing various image
processing methods such as image acquisition,
image
NASNETLARGE
enhancement,
binarization,
noise
removal, image inversion, image restoration,
cropping and resizing etc, we realized how
Deep Learning promotes development of
really advanced machine learning models. We
compared 10 models on a custom dataset made
from the signatures of VIT students and
recorded the appropriate results Finally with
From the obtained result we can see the mobilenet
model works the best in the in our custom dataset.
proper knowledge of python programming
language and conceptual Deep Learning, we
were able to develop a fairly accurate model
which could predict and distinguish between
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real and fraudulent signature
2516.
Thus with this project we conclude that Image
6.
Melo, V.K., Bezerra, B.L.D., Impedovo, D.,
processing is an advanced field of Computer
Pirlo, G. and Lundgren, A., 2019. Deep
Vision and Deep Learning and can be used
learning
extensively for many modern applications
handwritten signatures based on online
With proper utilization, this field has huge
samples. IET Biometrics, 8(3), pp.215-220
potential to completely transform the way
7.
approach to generate offline
Khan, M.J., Yousaf, A., Abbas, A.
humans interact with its surroundings and
and Khurshid, K., 2018. Deep
among themselves. This project has opened
learning for automated forgery
new horizons for us to explore and has really
detection in hyperspectral document
given us boosting motivation to explore this
images. Journal of Electronic Imaging,
adventurous fieldof Deep Learning and Image
Processing.
27(5), p.053001.
8.
Pashine, S., Dixit, R. and Kushwah, R.,
2021. Handwritten Digit Recognition
7
using Machine and Deep Learning
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