Result Details

ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits

SEKANINA, L.; MRÁZEK, V. ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits. In Proceedings of the Great Lakes Symposium on VLSI 2026. New York, NY, USA: Association for Computing Machinery, 2026. p. 983-989. ISBN: 9798400724312.
Type
conference paper
Language
English
Authors
Abstract

Approximate circuits often achieve exceptional trade-offs between computational accuracy and hardware efficiency, making them attractive for deployment as reusable Intellectual Property (IP) cores. However, safeguarding such circuits against piracy is critical for enabling sustainable commercialization of approximate computing. This work addresses the emerging challenge of IP protection and piracy detection in the context of approximate hardware. We introduce a novel adversarial threat model, approximate obfuscation, in which an attacker not only conceals the design through structural obfuscation but also introduces functional modifications to ensure that the resulting circuit exhibits nearly identical error characteristics and hardware metrics as the original IP.
To counter this threat, we propose an automated framework that extracts and compares statistical error profiles of protected IP cores and suspicious circuits, enabling systematic detection of potential IP theft. Through extensive experiments on a diverse set of approximate multipliers, we analyze the resilience of different approximate multipliers against approximate obfuscation. Our results provide new insights into the interplay between obfuscation, approximation, and IP protection.

Keywords

Approximate computing, IP theft attack, Approximate obfuscation

URL
Published
2026
Pages
7
Proceedings
Proceedings of the Great Lakes Symposium on VLSI 2026
Conference
Great Lakes Symposium on VLSI 2026
ISBN
9798400724312
Publisher
Association for Computing Machinery
Place
New York, NY, USA
DOI
EID Scopus
BibTeX
@inproceedings{BUT202019,
  author="Lukáš {Sekanina} and Vojtěch {Mrázek}",
  title="ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits",
  booktitle="Proceedings of the Great Lakes Symposium on VLSI 2026",
  year="2026",
  pages="7",
  publisher="Association for Computing Machinery",
  address="New York, NY, USA",
  doi="10.1145/3787109.3815215",
  isbn="9798400724312",
  url="https://dl.acm.org/doi/10.1145/3787109.3815215"
}
Projects
EvoML-EDA: Synergy of Evolutionary Algorithms and Advanced Machine Learning Algorithms for Digital Circuit Design, GACR, JUNIOR STAR, GM26-22525M, start: 2026-01-01, end: 2030-12-31, running
Departments
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