Trusting artificial intelligence in cybersecurity is a double-edged sword
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[1] Atul Prakash,et al. Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Chang Liu,et al. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning , 2018, 2018 IEEE Symposium on Security and Privacy (SP).
[3] Fabio Roli,et al. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2017, Pattern Recognit..
[4] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[5] Mariarosaria Taddeo,et al. Trust in Technology: A Distinctive and a Problematic Relation , 2010 .
[6] Vijay Kumar,et al. The grand challenges of Science Robotics , 2018, Science Robotics.
[7] Mariarosaria Taddeo,et al. Modelling Trust in Artificial Agents, A First Step Toward the Analysis of e-Trust , 2010, Minds and Machines.
[8] Tariq M. King,et al. AI for Testing Today and Tomorrow: Industry Perspectives , 2019, 2019 IEEE International Conference On Artificial Intelligence Testing (AITest).
[9] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[10] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[11] L. Floridi,et al. Regulate artificial intelligence to avert cyber arms race , 2018, Nature.
[12] Mariarosaria Taddeo,et al. Trusting Digital Technologies Correctly , 2017, Minds and Machines.
[13] Edward H. Glaessgen,et al. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .