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Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective
Artificial Intelligence has an important place in the scientific communi...
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Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999)
This is the Proceedings of the Fifteenth Conference on Uncertainty in Ar...
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Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments
This paper provides interested beginners with an updated and detailed in...
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Hybrid Systems for Knowledge Representation in Artificial Intelligence
There are few knowledge representation (KR) techniques available for eff...
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Analysis of Fleet Modularity in an Artificial Intelligence-Based Attacker-Defender Game
Because combat environments change over time and technology upgrades are...
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A Training-based Identification Approach to VIN Adversarial Examples
With the rapid development of Artificial Intelligence (AI), the problem ...
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A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control
The rapid developments of Artificial Intelligence in the last decade are...
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Techniques for Adversarial Examples Threatening the Safety of Artificial Intelligence Based Systems
Artificial intelligence is known as the most effective technological field for rapid developments shaping the future of the world. Even today, it is possible to see intense use of intelligence systems in all fields of the life. Although advantages of the Artificial Intelligence are widely observed, there is also a dark side employing efforts to design hacking oriented techniques against Artificial Intelligence. Thanks to such techniques, it is possible to trick intelligent systems causing directed results for unsuccessful outputs. That is critical for also cyber wars of the future as it is predicted that the wars will be done unmanned, autonomous intelligent systems. Moving from the explanations, objective of this study is to provide information regarding adversarial examples threatening the Artificial Intelligence and focus on details of some techniques, which are used for creating adversarial examples. Adversarial examples are known as training data, which can trick a Machine Learning technique to learn incorrectly about the target problem and cause an unsuccessful or maliciously directed intelligent system at the end. The study enables the readers to learn enough about details of recent techniques for creating adversarial examples.
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