ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits

• URL: http://arxiv.org/abs/2605.10355v1
• Date: Mon, 11 May 2026 11:01:49 GMT
• Title: ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits
• Abstract summary: This work addresses the emerging challenge of IP protection and piracy detection in the context of approximate hardware.<n>We introduce a novel adversarial threat model, approximate obfuscation, in which an attacker conceals the design through structural obfuscation.<n>We propose an automated framework that extracts and compares statistical error profiles of protected IP cores and suspicious circuits.
• Score: 0.05156484100374057
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
  Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
  arXiv  Detail & Related papers  (2026-05-20T22:17:29Z)
• TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning [60.68349524623048]
  Decompositional jailbreaks pose a critical threat to large language models.<n>We present TwinGate, a stateful dual-encoder defense framework.<n>We construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents.
  arXiv  Detail & Related papers  (2026-04-30T13:44:01Z)
• Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection [1.7331553371038069]
  This paper presents an agentic, large language model (LLM)-driven framework for automated hardware netlist obfuscation.<n>Rather than a single prompt-to-output generation step, the framework decomposes the task into specialized stages for circuit analysis, synthesis, verification, and attack evaluation.<n>Results show that the framework generates correct locked netlists while introducing measurable output corruption under incorrect keys, while SAT attacks remain effective.
  arXiv  Detail & Related papers  (2026-04-14T21:04:48Z)
• CIPHR: Cryptography Inspired IP Protection through Fine-Grain Hardware Redaction [5.322112910399912]
  Hardware intellectual property (IP) in the globalized integrated circuit supply chain is exposed to a wide range of confidentiality and integrity attacks.<n>Existing IP-level countermeasures, such as logic locking, hardware obfuscation, camouflaging, and redaction, have aimed at addressing these attacks.<n>We present CIPHR, a robust fine-grain hardware redaction methodology inspired by the cryptographic property of indistinguishability.
  arXiv  Detail & Related papers  (2026-04-04T03:00:49Z)
• Disguising Topology and Side-Channel Information through Covert Gate- and ML-Enabled IP Camouflaging [2.156373334386171]
  IP theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques.<n>Traditional cryptic'' IC camouflaging methods focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis.<n>We show that a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A)<n>Our results validate that this multi-layered approach effectively thwarts the entire RE toolchain by poisoning the structural and logical data used for netlist understanding.
  arXiv  Detail & Related papers  (2026-03-26T20:53:10Z)
• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
  Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
  arXiv  Detail & Related papers  (2025-06-03T01:37:09Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting.<n>Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines.<n> Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Library-Attack: Reverse Engineering Approach for Evaluating Hardware IP Protection [4.873538793723815]
  Existing countermeasures for hardware IP protection, such as obfuscation, camouflaging, and redaction, aim to defend against confidentiality and integrity attacks.<n>Within the current threat model, these techniques overlook the potential risks posed by a highly skilled adversary with privileged access to the IC supply chain.<n>We introduce Library-Attack, a novel reverse engineering technique that leverages privileged design information and prior knowledge of security countermeasures to recover sensitive hardware IP.
  arXiv  Detail & Related papers  (2025-01-21T17:01:18Z)
• Automated Hardware Logic Obfuscation Framework Using GPT [3.1789948141373077]
  We introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security.
  arXiv  Detail & Related papers  (2024-05-20T17:33:00Z)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.