Authors:
László Vidács MTA-SZTE Mesterséges Intelligencia Kutatócsoport [University of Szeged and Hungarian Academy of Sciences, MTA-SZTE Research Group on Artificial Intelligence] Szeged Hungary

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Márk Jelasity Szegedi Tudományegyetem Számítógépes Algoritmusok és Mesterséges Intelligencia Tanszék [University of Szeged, Department of Algorithms and AI] Szeged Hungary

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László Tóth Szegedi Tudományegyetem Szoftverfejlesztési Tanszék [University of Szeged, Department of Software Engineering] Szeged Hungary

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Péter Hegedűs MTA-SZTE Mesterséges Intelligencia Kutatócsoport [University of Szeged and Hungarian Academy of Sciences, MTA-SZTE Research Group on Artificial Intelligence] Szeged Hungary

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Rudolf Ferenc Szegedi Tudományegyetem Szoftverfejlesztési Tanszék [University of Szeged, Department of Software Engineering] Szeged Hungary

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Összefoglalás. A mély mesterséges neuronhálók elterjedése az ipari alkalmazásokban évekkel azok megbízhatóságával, értelmezhetőségével, és biztonságával kapcsolatos szakterületek fejlődését megelőzően történt. Az egyik, gyakorlatban is jelentős területen, a képfelismerésben például a megvalósult megoldások szinte már emberi teljesítményre képesek, de ezzel együtt célzott zajjal ezek a rendszerek félrevezethetők, megzavarhatók. Jelen kéziratban ismertetünk néhány tipikus biztonsági problémát, valamint rámutatunk arra, hogy a hagyományos szoftverfejlesztés területén alkalmazott minőségbiztosítási módszerekkel rokon megoldásokra szükség van az MI-re épülő rendszerek fejlesztésében, akár a mesterséges neuronhálók biztonságát, akár az MI rendszerek hagyományos komponenseinek fejlesztését tartjuk szem előtt.

Summary. Research on the trustworthiness, interpretability and security of deep neural networks lags behind the widespread application of the technology in industrial applications. For example, in image recognition, modern solutions are capable of nearly human performance. However, with targeted adversarial noise, these systems can be arbitrarily manipulated. Here, we discuss some of the security problems and point out that quality assurance methods used in traditional software development should also be adapted when developing AI-based systems, whether in the security of artificial neural networks or traditional components of AI systems. One of the main concerns about neural networks today that – to the best of our knowledge – affects all deep neural networks is the existence of adversarial examples. These examples are relatively easy to find and according to a recent experiment, a well-chosen input can attack more networks at the same time. In this paper we also present a wider perspective of security of neural architectures borrowed from the traditional software engineering discipline. While in traditional development several methods are widely applied for software testing and fault localization, there is a lack of similar well-established methods in the neural network context. In case of deep neural networks, systematic testing tools and methods are in the early stage, and a methodology to test and verify the proper behavior of the neural networks is highly desirable. Robustness testing of machine learning algorithms is a further issue. This requires the generation of large random input data using fuzz testing methods. The adaptation of automatic fault localization techniques has already started by defining notions like code coverage to neural networks. Lastly, we argue that the effective development of high quality AI-based systems need well suited frameworks that can facilitate the daily work of scientists and software developers – like the Deep-Water framework, presented in the closing part of the paper.

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Editor-in-Chief:

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  • Zoltán BIRKNER (University of Pannonia)
  • Valéria CSÉPE (Research Centre for Natural Sciences, Brain Imaging Centre)
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  • Charaf HASSAN (Budapest University of Technology and Economics)
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  • Gábor KOVÁCS (University of Public Service)
  • Levente KOVÁCS buda University)
  • Melinda KOVÁCS (Hungarian University of Agriculture and Life Sciences (MATE))
  • Miklós MARÓTH (Avicenna Institue of Middle Eastern Studies )
  • Judit MÓGOR (Ministry of Interior National Directorate General for Disaster Management)
  • József PALLO (University of Public Service)
  • István SABJANICS (Ministry of Interior)
  • Péter SZABÓ (Hungarian University of Agriculture and Life Sciences (MATE))
  • Miklós SZÓCSKA (Semmelweis University)

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Scientia et Securitas
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