Evasion Attack for Fingerprint Biometric System and Countermeasure
Currently, biometrics is being widely used for authentication and identification of an individual. The biometric systems itself needs to be more secured and reliable so it they can provide secure authentication in various applications. To optimize the security, it is vital that biometric authentication frameworks are intended to withstand various sources of attack. In security sensitive applications, there is a shrewd adversary component which intends to deceive the detection system. In a well-motivated attack scenario, in which there exists an attacker who may try to evade a well-established system at test time by cautiously altering attack samples, i.e., Evasion Attack. The aim of this work is to demonstrate that machine learning can be utilized to enhance system security, if one utilizes an adversary-aware approach that proactively intercept the attacker. Also, we present a basic but credible gradient based approach of evasion attack that can be exploited to methodically acquire the security of a Fingerprint Biometric Database.
KeywordsAdversarial machine learning Evasion attacks Convolution neural networks Biometrics Biometrics security Fingerprint Classifier
We would like to express our deep and sincere gratitude to Dr. Manvjeet Kaur for her invaluable encouragement, suggestions, and support in this study and research. We would also like to acknowledge all the cited authors in this study. We would like to express our thanks to all those who contributed in many ways to the success of this study. With the best of our knowledge, all the information provided is authentic and revised.
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