Mitigating Presentation Attack using DCGAN and Deep CNN

TL;DR

This paper presents a method combining DCGAN-generated synthetic images and deep CNNs to effectively detect presentation attacks in biometric systems, achieving high accuracy across multiple datasets.

Contribution

The work introduces a novel approach using DCGAN for synthetic image augmentation and deep CNNs for attack detection in biometric authentication.

Findings

Achieved up to 97% accuracy on iris attack detection.

Effective in both controlled and uncontrolled environments.

Applicable to facial and iris biometric datasets.

Abstract

Biometric based authentication is currently playing an essential role over conventional authentication system; however, the risk of presentation attacks subsequently rising. Our research aims at identifying the areas where presentation attack can be prevented even though adequate biometric image samples of users are limited. Our work focusses on generating photorealistic synthetic images from the real image sets by implementing Deep Convolution Generative Adversarial Net (DCGAN). We have implemented the temporal and spatial augmentation during the fake image generation. Our work detects the presentation attacks on facial and iris images using our deep CNN, inspired by VGGNet [1]. We applied the deep neural net techniques on three different biometric image datasets, namely MICHE I [2], VISOB [3], and UBIPr [4]. The datasets, used in this research, contain images that are captured both in controlled and uncontrolled environment along with different resolutions and sizes. We obtained the best test accuracy of 97% on UBI-Pr [4] Iris datasets. For MICHE-I [2] and VISOB [3] datasets, we achieved the test accuracies of 95% and 96% respectively.