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Artificial neural networks tend to learn only what they need for a task.
Feature-based image metamorphosis
Beier, T., Neely, S.: · 1992
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Poisson image editing
Pérez, P., Gangnet, M., Blake, A.: · 2003
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A high-resolution 3d dynamic facial expression database
Yin, L., Chen, X., Sun, Y., Worm, T., Reale, M.: · 2008
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The put face database
Kasiński, A., Florek, A., Schmidt, A.: · 2008
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Dlib-ml: A machine learning toolkit
King, D.E.: · 2009
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A new ranking method for principal components analysis and its application to face image analysis
Thomaz, C.E., Giraldi, G.A.: · 2010
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Scface — surveillance cameras face database
Grgic, M., Delac, K., Grgic, S.: · 2011
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The magic passport
Ferrara, M., Franco, A., Maltoni, D.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2014
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One millisecond face alignment with an ensemble of regression trees
Kazemi, V., Sullivan, J.: · 2014
Cited alongside, same era.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., Szegedy, C.: · 2015
Cited alongside, same era.
The chicago face database: A free stimulus set of faces and norming data
Ma, D., Correll, J., Wittenbrink, B.: · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Detecting morphed face images
Raghavendra, R., Raja, K.B., Busch, C.: · 2016
Cited alongside, same era.
Detection of face morphing attacks by deep learning
Seibold, C., Samek, W., Hilsmann, A., Eisert, P.: · 2017
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Transferable deep-cnn features for detecting digital and print-scanned morphed face images
Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: · 2017
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Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: · 2017
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Simple black-box adversarial attacks on deep neural networks
Narodytska, N., Kasiviswanathan, S.P.: · 2017
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Evading classifiers by morphing in the dark
Dang, H., Huang, Y., Chang, E.C.: · 2017
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The layer-wise relevance propagation toolbox for artificial neural networks
Lapuschkin, S., Binder, A., Montavon, G., Müller, K.R., Samek, W.: · 2016
Cited alongside, same era.
Automatic generation and detection of visually faultless facial morphs
Makrushin, A., Neubert, T., Dittmann, J.: · 2017
Cited alongside, same era.
Face morphing detection: An approach based on image degradation analysis
Neubert, T.: · 2017
Cited alongside, same era.
Color feret database
Phillips, P.J.:
Cited in the paper.
Utrecht ecvp
Hancock, P.:
Cited in the paper.
Su, J., Vargas, D.V., Sakurai, K.: · 2017
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Face demorphing
Ferrara, M., Franco, A., Maltoni, D.: · 2018
Closest in time.
Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models
Samek, W., Wiegand, T., Müller, K.R.: · 2018
Closest in time.
Srinivasan, V., Marban, A., Müller, K.R., Samek, W., Nakajima, S.: · 2018
Closest in time.