Deep Neural Networks Aiding Cryptanalysis: A Case Study of the Speck Distinguisher

Norica Băcuieți*, Lejla Batina, Stjepan Picek

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


At CRYPTO’19, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear; therefore, in this paper, we study the depth-10 and depth-1 neural distinguishers proposed by Gohr [7] with the aim of finding out whether smaller or better-performing distinguishers for Speck32/64 exist. We first evaluate whether we can find smaller neural networks that match the accuracy of the proposed distinguishers. We answer this question in the affirmative with the depth-1 distinguisher successfully pruned, resulting in a network that remained within one percentage point of the unpruned network’s performance. Having found a smaller network that achieves the same performance, we examine whether its performance can be improved as well. We also study whether processing the input before giving it to the pruned depth-1 network would improve its performance. To this end, convolutional autoencoders were found that managed to reconstruct the ciphertext pairs successfully, and their trained encoders were used as a preprocessor before training the pruned depth-1 network. We found that, even though the autoencoders achieved a nearly perfect reconstruction, the pruned network did not have the necessary complexity anymore to extract useful information from the preprocessed input, motivating us to look at the feature importance to get more insights. To achieve this, we used LIME, with results showing that a stronger explainer is needed to assess it correctly.

Original languageEnglish
Title of host publicationApplied Cryptography and Network Security - 20th International Conference, ACNS 2022, Proceedings
EditorsGiuseppe Ateniese, Daniele Venturi
Number of pages21
ISBN (Print)978-3-031-09233-6
Publication statusPublished - 2022
Event20th International Conference on Applied Cryptography and Network Security, ACNS 2022 - Virtual, Online
Duration: 20 Jun 202223 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13269 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Applied Cryptography and Network Security, ACNS 2022
CityVirtual, Online


  • Feature importance
  • Neural distinguisher
  • Pruning
  • Speck


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