Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks
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Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.
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