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Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspection line without the involvement of officers.
A new optimizer using particle swarm theory
Eberhart, R., and Kennedy, J · 1995
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Use of invisible noise signals to prevent privacy invasion through face recognition from camera images
Yamada, T., Gohshi, S., and Echizen, I · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Understanding osn-based facial disclosure against face authentication systems
Li, Y., Xu, K., Yan, Q., Li, Y., and Deng, R. H · 2014
Earlier work this paper cites.
Deep learning face representation from predicting 10,000 classes
Sun, Y., Wang, X., and Tang, X · 2014
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Deepface: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
Earlier work this paper cites.
Deepid3: Face recognition with very deep neural networks
Sun, Y., Liang, D., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
High-fidelity pose and expression normalization for face recognition in the wild
Zhu, X., Lei, Z., Yan, J., Yi, D., and Li, S. Z · 2015
Cited alongside, same era.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2016
Cited alongside, same era.
Simple black-box adversarial perturbations for deep networks
Narodytska, N., and Kasiviswanathan, S. P · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Is attacking machine learning easier than defending it?
Goodfellow, I., and Papernot, N · 2017
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Machine learning as an adversarial service: Learning black-box adversarial examples
Hayes, J., and Danezis, G · 2017
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Blocking transferability of adversarial examples in black-box learning systems
Hosseini, H., Chen, Y., Kannan, S., Zhang, B., and Poovendran, R · 2017
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Adversarial attacks on neural network policies
Huang, S., Papernot, N., Goodfellow, I., Duan, Y., and Abbeel, P · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Papernot, N., McDaniel, P., and Goodfellow, I · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
Cited alongside, same era.
Towards the science of security and privacy in machine learning
Papernot, N., McDaniel, P., Sinha, A., and Wellman, M · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M. K · 2016
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N., and Wagner, D · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N., and Wagner, D · 2017
Cited alongside, same era.
Robust physical-world attacks on deep learning models
Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., and Song, D
Cited in the paper.
Later among the works it cites.
Why does infrared appear purple in digital cameras
McClatchie, I · 2017
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Dolphinattack: Inaudible voice commands
Zhang, G., Yan, C., Ji, X., Zhang, T., Zhang, T., and Xu, W · 2017
Later among the works it cites.
en.wikipedia.org/wiki/Sunlight
Sunlight · 2018
Closest in time.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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