ML-In-Cybersecurity-Paper/bibliography.tex at master · tugot17/ML-In-Cybersecurity-Paper · GitHub

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Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market
Deployment to Future Shifts in Business Models

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Sharif, Bhagavatula, Bauer, Reiter
Accessorize to a Crime: Real and Stealthy Attacks on
State-of-the-Art Face Recognition


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Hardy,
https://www.nytimes.com/2016/07/19/technology/reasons-to-believe-the-ai-boom-is-real.html

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Krizhevsky, Sutskeve, Hinton,
ImageNet Classification with Deep Convolutional
Neural Networks

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Awad
Machine Learning Techniques for Fingerprint
Identification: A Short Review

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Parkhi, Vedaldi, Zisserman
Deep Face Recognition


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N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K.
Nayar. Attribute and simile classifiers for face
verification. In Proc. ICCV, 2009

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Goodfellow, Shlens, Szegedy,
Explaining and Harnessing Adversarial Examples


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Szegedy et al.
Intriguing properties of neural networks

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Megvii Inc. Face++. http://www.faceplusplus.com/.

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Convolution Operation on Volume, Multiple Filters, Stride, Padding, Pooling, LeNet 5 \url{https://indoml.com/2018/03/07/student-notes-convolutional-neural-networks-cnn-introduction/}

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Szegedy et al.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015.

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M. Fredrikson, S. Jha, and T. Ristenpart. Model
inversion attacks that exploit confidence information
and basic countermeasures. In Proc. ACM CCS, 2015.