Title: APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREVENT ATM FRAUD BY FACIAL DIS-GUISE IDENTIFICATION

Abstract: The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework Singh et al. (2017) proves the applicability of deep neural networks for this very problem. All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users. However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage. The proposed deep convolutional neural network is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not. The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction. The network is trained using a dataset of images captured in similar situations as of an ATM. The comparatively low background clutter in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.

1. INTRODUCTION:

The widespread acceptance of Automated Teller Machine (ATM) and their omnipresence in the banking sector has engendered numerous security concerns. One of the most imperative concerns being, verifying the authenticity of the user. Evidently, most of the ATMs across the globe simply rely on a card and a Personal Identification Number (PIN) for authentication. However, in either case, it is plausible that the user is not authorised to transact. For instance, illegal practices like phishing, shoulder surfing, card fraud, stolen card can cause substantial monetary loss to the owner.

To overcome and identify such practices, ATMs have an inbuilt camera which records 24x7. The current state of art ATM security works in the following way: After a fraudulent transaction, the owner of the corresponding bank account reports about the fraud. The police then investigates and goes through the footage recorded by the ATM camera to find the face of the imposter. Once the face is identified, the police searches for the imposter. Clearly, this security measure can be easily gamed by using artifacts or alterations like wigs, caps, eyeglasses, beard to cover the face for intentional disguises. As a result, Righi et al. (2012) stated that such face alterations can substantially degrade the performance of the system. Hence, this approach has a very low success rate which is unacceptable in banking sectors. Additionally, Sharma (2012) explained different openings and vulnerabilities that exist at the time of transactions due to fake entries and fake cards.

Apparently, this chaos can be prevented by ensuring that the transaction proceeds only if the face is undisguised and reveal identity of the user. The proposed system extracts the user's face from the footage and checks if the face is disguised. The system is trained cleverly to identify such faces by an extensive pool of disguised and undisguised faces. If the face is disguised, the system will not allow the transaction to be proceeded, thereby preventing the imposter from stealing.

To achieve this, the proposed system uses Deep Convolutional Neural Networks for image classification using statistical dimensionality reduction method. Deep networks have proved to be exceptional in computer vision problems (Sun et al., 2013) (Haavisto et al., 2013) . Sun et al. (2013) stated a three-layer cascading style which superficially captures the high level features and refines them to detect deeper features. Analogously, the proposed system uses a five-layer architecture, first 3 layers comprises of a convolutional layers followed by a pooling layers to learn the features of the following types of images : Disguised, Partially disguised and Undisguised.

2. PROPOSED SYSTEM:

2.1 EXISTING MECHANISMS Plenty of research work has been published in response to the ATM security problems and a lot of it relates to using machine learning to authenticate users. Fagbolu et al. (2014) proposed a facebased authentication as identity test for users and the system uses facial recognition with biometric features. T. Suganya (2015) stated the applicability of image processing by amalgamation of Face Recognition System (FRS) in the identity verification process engaged in ATMs. Dhamecha et al. (2013) proposed a framework to classify local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (regions with disguise) classes and used the biometric patches for facial feature extraction and matching.

2.2. PROBLEMS & LIMITATIONS:

The fact that none of the above mentioned mechanisms are implemented in the current state-ofart endorses that they are not at par with the requirements and have to be compromised due to certain trade-offs. The mechanisms use an extensive pool of user information and glut of computing resources such as cloud storage, network bandwidth which makes them in-feasible and erratic .

According to T. Suganya ( 2015), transactor's picture should be clicked and matched to existing records after his card and PIN are verified. However, such computation cannot be relied upon as it highly depends on the remote server which holds the data and the available bandwidth. Such systems try to authenticate too much and exceed the computational limits of the machine. Also, emergency situations where the card owner is unavailable to authenticate the transaction is where the current systems (Fagbolu et al., 2014 ), (T. Suganya, 2015), (Kibona, 2015) suffer. Moreover, with reference to Das & Debbarma (2011) , clearly Eigenface based method can be spoofed by using face masks or photos of account holder.

2.3. PROPOSED SOLUTION:

The proposed solution takes advantage of the fact that fraudsters hide their identity to complete the transaction and not get caught in police investigation. To achieve this, fraudsters cover their faces with disguises while performing the transaction. We propose and demonstrate the use of a Convolutional Neural Network to identify face disguises, thereby preventing fraudulent transactions. An ATM equipped with the proposed DCNN model will not allow any user with disguised face to complete the transaction and prompt for removing the disguise to complete the transaction. The DCNN is trained to identify in real time whether the user interacting with the ATM is having disguised face or not without consuming any additional data or remote resources. The output is swiftly reported to the ATM to further proceed the transaction based on the computed label. The proposed system makes sure that every user interacting with the ATM shows his/her identity to the ATM and essentially allows police to identify the user corresponding to a fraud transaction.

3. THE DCNN MODEL:

The DCNN model is implemented in python 2.7 using TensorFlow(r1.3) and is executed on an Ubuntu 16.04 operating system. Stochastic Gradient Descent method is used to train the model and Kingma and Ba's Adam algorithm to control the learning rate.

3.1. DATASET:

The dataset used to train and test the contains 1986 images of faces covered with any combination of 4 disguises namely scarf, helmet, eyeglasses and fake beard. The images are published by Singh et al. (2017) and matches the requirement to an acceptable level. The images are manually classified in 3 classes, which are -disguised, undisguised and partially disguised. Fig. 1 exemplifies the dataset in short. While feeding to the network the 1986 images were randomly split into 1500 and 486 images which is approximately 75% and 25% split ratio. 1500 images were used for training and remaining 486 for testing. 30 batches of 50 randomly chosen samples were used for every training cycle. After every training cycle, the training set was shuffled to randomize the data and ensure generalization in learning process. • Disguised Faces which are not recognizable by humans are labelled as disguised. These images particularly contain more than one disguises and effectively hide the identity of the person in the image. 1372 images belong to disguised class.

• Partially Disguised:

We introduced this label to adopt the network to allow users having unintentional disguises such as spectacles and natural beard. These images are recognizable by humans and the apparent disguises are part of the persons identity. There are 212 samples of partially disguised faces.

• Undisguised:

Faces showing clear and effective identity of the person in the image are labelled as undisguised. A total of 402 images belong to the undisguised class.

3.2. NETWORK CHARACTERISTICS:

The Rectified Linear Unit (ReLU) activation function is used to model the neurons. The following equation defines ReLU function.

EQUATION

The activation is simply thresholded at zero. Due to its linear and non-saturating form, it significantly accelerates the convergence of Stochastic Gradient Descent (SGD) compared to the sigmoid or tanh functions. The network parameters are initialised with a standard deviation of 0.1 to facilitate SGD. The training steps are executed using the tensorflow.train.AdamOptimizer function in TensorFlow, with 1e-4 as the initial learning rate and cross entropy as the cost functions needed to be optimized. The AdamOptimizer uses Kingma and Ba's Adam algorithm to control the learning rate. Adam offers several advantages and foremost is that it uses moving averages of the parameters (momentum). Bengio (2012) ] discusses the reasons for why this is beneficial. Simply put, this enables Adam to use a larger effective step size, and the algorithm will converge to this step size without fine tuning.

3.3. NETWORK ARCHITECTURE AND PARAMETERS:

The DCNN model contains 3 convolutional layers each followed by a pooling layer. After the 3rd pooling layer two fully connected hidden layers are introduced to classify the features learned by the convolutional layers. The output layer uses softmax function to calculate probabilities of the image to belong to each class. 

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Figures and Tables:

Figure 1: Examples of 2 images of each disguised, partially disguised and undisguised classes respectively.

function is used to classify each sample and present the predicted output to cost function to calculate training error. Cross entropy is the loss measure used to measure training error of the learner and it is as defined below of Samples in the Batch Y : [3x1] Size One-Hot Encoded Vector Showing Correct Classification Label O : [3x1] Size Probability Distribution Vector Obtained from Softmax Function in the Output Layer

Figure2shows the model architecture along with specific network parameters and respective input and output image dimensions. The first convolutional layer uses 32 feature maps with kernel dimensions as (7x7x3). Similarly, the 2nd and 3rd convolutional layers use 64 and 128 feature maps and kernels with dimensions (5x5x32) and (5x5x64) respectively. All 3 pooling layers use (2x2) max pooling with stride length of 2. The 3 convolutional layers are accompanied with bias vectors of sizes (32x1), (64x1), (128x1) respectively. The following 2 fully connected hidden layers have weight matrices of dimensions (512x18432) and (512x512) respectively and bias vectors of size (512x1) each. The output layer contains 3 neurons and weight matrix of size (3x512) with (3x1) sized bias vector. In all, the network incorporates 9,962,819 trainable parameters which are eventually learned by the network in training step and used to classify the unknown examples while testing.

Figure 2: Network architecture and Parameters

Figure 3: Evolution of test accuracy over 60 training cycles

Figure 5: Input image for which activations in Figure 6,7,8 are obtained

Peer review (iclr2018):
 As one can see by the title, the originality (application of DCNN) and significance (limited to ATM domain) is very limited. If this is still enough for ICLR, the paper could be okay. However, even so one can clearly see that the architecture, the depth, the regularization techniques, and the evaluation are clearly behind the state of the art. Especially for this problem domain, drop-out and data augmentation should be investigated. Only one dataset is used for the evaluation and it seems to be very limited and small. Moreover, it seems that the same subjects (even if it is other pictures) may appear in the training set and test set as they were randomly selected. Looking into the referece (to get the details of the dataset - from a workshop of the IEEE International Conference on Computer Vision Workshops (ICCVW) 2017) reveals, that it has only 25 subjects and 10 disguises. This makes it even likely that the same subject with the same disguise appears in the training and test set. A very bad manner, which unfortunately is often performed by deep learning researchers with limited pattern recognition background, is that the accuracy on the test set is measured for every timestamp and finally the highest accuracy is reported. As such you perform an optimization of the paramerter #iterations on the test set, making it a validation set and not an independent test set. Minor issues: make sure that the capitalization in the references is correct (ATM should be capital, e.g., by putting {ATM} - and many more things).