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.

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