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Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks

Siyue Wang, Xiao Wang, Pin-Yu Chen, Pu Zhao, Xue Lin
2021 Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence   unpublished
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated  ...  To achieve better trade-off between robustness and transferability, we propose three kinds of characteristic examples: vanilla C-examples, RC-examples, and LTRC-example, to derive fingerprints from the  ...  Characteristic Examples for Neural Network Fingerprinting It is a non-trival task to design C-examples that are both robust to 2 Pruned Models and exhibiting low-transferability on 3 Other Models.  ... 
doi:10.24963/ijcai.2021/80 fatcat:5bcvp2jfhragtcrz2gqnqp5wha

A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

Da Li, Zhao Niu
2021 ISPRS International Journal of Geo-Information  
Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system.  ...  Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization  ...  The batch normalization layer converts the distribution of the input values of the neural network into a normal distribution, making the model more robust.  ... 
doi:10.3390/ijgi10070442 fatcat:6d6np7gfhfbpnost7s2rcuqq5a

DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks [article]

Huili Chen and Bita Darvish Rohani and Farinaz Koushanfar
2018 arXiv   pre-print
Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks  ...  DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning.  ...  The fingerprints are embedded in the first geometry in a high dimensional space, the number of users convolutional layer of the underlying neural network.  ... 
arXiv:1804.03648v1 fatcat:iday65xthjdpzcg52h3zzpy67u

Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Dogus Karabulut, Pavlo Tertychnyi, Hasan Sait Arslan, Cagri Ozcinar, Kamal Nasrollahi, Joan Valls, Joan Vilaseca, Thomas B. Moeslund, Gholamreza Anbarjafari
2020 Multimedia tools and applications  
For the evaluation of the proposed enhancement technique, we use VGG16 based convolutional neural network to assess the percentage of enhanced fingerprint images which are labelled correctly as undistorted  ...  We use a database of low quality fingerprint images containing 11541 samples with dryness, wetness, blurriness, damages and dotted distortions.  ...  [35] , for instance, developed an efficient, yet high accuracy, deep neural network algorithm to recognize low quality fingerprints.  ... 
doi:10.1007/s11042-020-08750-8 fatcat:6ieckwp22zcnjhqbsjrvzxluhi

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Pavol Marák, Alexander Hambalík
2016 Tatra Mountains Mathematical Publications  
Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks  ...  From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.  ...  Level-2 feature extraction based on neural network also proved to be reliable since Level-2 extraction accuracy reached 98.64 % on the mixed high and low quality fingerprint database.  ... 
doi:10.1515/tmmp-2016-0035 fatcat:dqkawpizkjg3ro75qbrpuhzqk4

The Adaptive Fingerprint Localization in Dynamic Environment

Keliu Long, Chongwei Zheng, Kun Zhang, Chuan Tian, Chong Shen
2022 IEEE Sensors Journal  
After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI  ...  of low cost and widely deployment.  ...  For more information, see This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/jsen.2022.3175742 fatcat:icaotnztozedtbrvfb27lqefvy

Review on effectiveness of deep learning approach in digital forensics

Sonali Ekhande, Uttam Patil, Kshama Vishwanath Kulhalli
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques.  ...  <p><span>Cyber forensics is use of scientific methods for definite description of cybercrime activities.  ...  This includes generative adversarial networks, deep belief network, recurrent neural network and convolutional neural network.  ... 
doi:10.11591/ijece.v12i5.pp5481-5592 fatcat:oyale5kuljcjvh54mkztfmcqde

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples [article]

Nils Lukas, Yuxuan Zhang, Florian Kerschbaum
2021 arXiv   pre-print
We propose a fingerprinting method for deep neural network classifiers that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such  ...  In Machine Learning as a Service, a provider trains a deep neural network and gives many users access.  ...  INTRODUCTION Deep neural network (DNN) classifiers have become indispensable tools for addressing practically relevant problems, such as autonomous driving (Tian et al., 2018) , natural language processing  ... 
arXiv:1912.00888v4 fatcat:sen7glewxfhv7cjq72zxvyqfqy

Fingerprint Presentation Attack Detection Using Deep Transfer Learning and DenseNet201 Network

Divine S. Ametefe, Suzi Seroja Sarnin, Darmawaty M. Ali
2021 International Journal of Electrical & Electronic Systems Research (IEESR)  
Motivated by this concern, we propose a deep transfer learning approach to automatically learn deep hierarchical semantic fingerprint features as a means of discriminating against spoofs.  ...  Experiments were conducted on the LivDet competition standard database, encompassing datasets from LivDet-2009, 2011, 2013, and 2015, resulting in the acquisition of real fingerprints and fake fingerprints  ...  Implementation of Deep Transfer Learning Using DenseNet201 The execution of pre-trained models is generally hinged on the principles of Convolution Neural Networks (CNN).  ... 
doi:10.24191/jeesr.v19i1.013 fatcat:ngjhipy33jfh3gmhtizuhakzne

Differential Contour Stellar Based Radio Frequency Fingerprint identification for Internet of Things

Jingchao Li, Yulong Ying, Chunlei Ji, Bin Zhang
2021 IEEE Access  
Data attacks from illegal access devices of the Internet of Things will cause serious interference and threats to the entire network.  ...  The proposed method can improve the effect of radio frequency fingerprint identification from three aspects: i.  ...  ACKNOWLEDGMENT This research is supported by the National Natural Science Foundation of China (No. 62076160, No.51806135 and 61603239).  ... 
doi:10.1109/access.2021.3071352 fatcat:lo3en6utnvgmll4xn4wlryhduy

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Sergio Saponara, Abdussalam Elhanashi, Qinghe Zheng
2021 IEEE Access  
The advantage of convolutional neural networks makes it suitable for feature extraction. Four datasets of fingerprint images have been used to prove the robustness of the proposed architecture.  ...  Deep learning, especially Convolutional Neural Network (CNN) has made a tremendous success in the field of computer vision for pattern recognition.  ...  ACKNOWLEDGMENT We thank the Islamic Development Bank for the support of the Ph.D. work of A. Elhanashi and the "Dipartimenti di Eccellenza -Crosslab" project by MIUR.  ... 
doi:10.1109/access.2021.3124746 fatcat:twzkbnr2lzafnnokw3oq6o4jua

RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network

Shayan Taheri, Milad Salem, Jiann-Shiun Yuan
2019 Big Data and Cognitive Computing  
The deep neural network gets its weights from transfer learning, adversarial training, and noise training.  ...  The shallow neural network has the duty of data preprocessing and generating adversarial samples.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bdcc3030043 fatcat:krg4bkbaejfzdadlzqgia6kiey

Lightweight Convolutional Neural Network based on singularity ROI for fingerprint classification

Wen Jian, Yujie Zhou, Hongming Liu
2020 IEEE Access  
In this paper, a Lightweight CNN (Convolutional Neural Network) structure based on singularity ROI (region of interest) is proposed.  ...  INDEX TERMS Fingerprint classification, SVM, KNN, lightweight, convolutional neural network. 54554 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Moreover, neural network has strong robustness and fault tolerance to noise, and neural network can fully approximate complex nonlinear relationships, which makes neural network promising in pattern recognition  ... 
doi:10.1109/access.2020.2981515 fatcat:ekr2xlyswfgc3d4rcs6xkfwu54

A Survey of Machine Learning for Indoor Positioning

Ahasanun Nessa, Bhagawat Adhikari, Fatima Hussain, Xavier Fernando
2020 IEEE Access  
Medium Medium Medium RSSI [81] Min-max Bluetooth Medium Medium Low Medium RSSI [82] K-NN WLAN Medium Low Low High RSSI [83] SVM, MLP WLAN Medium Low High High ToA,ToF [84] Fingerprinting  ...  High High Low TDoA [87] Fingerprinting Wi-Fi High High High Medium TDoA [51] Least Square UWB Medium Medium to High High High TDoA+AoA [88] Least Square UWB Medium Medium to  ... 
doi:10.1109/access.2020.3039271 fatcat:htzgf2mwp5gmjbx3cczg5rl7ru

Wireless Localization Based on Deep Learning: State of Art and Challenges

Yun-Xia Ye, An-Nan Lu, Ming-Yi You, Kai Huang, Bin Jiang, Jian Feng Li
2020 Mathematical Problems in Engineering  
The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models.  ...  The problem of position estimation has always been widely discussed in the field of wireless communication.  ...  Acknowledgments is work was support by No. 36 Research Institute of CETC under the project no. CX05.  ... 
doi:10.1155/2020/5214920 fatcat:fsotemsxnfahzbmsxlrmiecrp4
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