# Mitigating Presentation Attack using DCGAN and Deep CNN

arXiv: https://arxiv.org/abs/2207.00161 · HTML: https://mlsanity.com/paper/2207.00161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [Deep Representations for Iris, Face, and Fingerprint Spoofing Detection](https://mlsanity.com/paper/1410.1980/) (2014)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [Named Entity Recognition in Unstructured Medical Text Documents](https://mlsanity.com/paper/2110.15732/) (2021)
- [A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation](https://mlsanity.com/paper/2201.12705/) (2022)
- [IoT Security and Authentication schemes Based on Machine Learning: Review](https://mlsanity.com/paper/2109.02695/) (2021)
- [Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review](https://mlsanity.com/paper/2202.02456/) (2022)

## Abstract (arXiv)

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.