IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2021

AUTHORS

Hossein Hajipour , Mateusz Malinowski , Mario Fritz

ABSTRACT

In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases – even exceeding prior work that had privileged information on the black-box. More... »

PAGES

143-157

Book

TITLE

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

ISBN

978-3-030-93732-4
978-3-030-93733-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-93733-1_10

DOI

http://dx.doi.org/10.1007/978-3-030-93733-1_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1145698178


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