IrisFP: Adversarial-Example-based Model Fingerprinting with Enhanced Uniqueness and Robustness

• URL: http://arxiv.org/abs/2603.24996v2
• Date: Mon, 30 Mar 2026 16:29:29 GMT
• Title: IrisFP: Adversarial-Example-based Model Fingerprinting with Enhanced Uniqueness and Robustness
• Abstract summary: IrisFP is a novel adversarial-example-based model fingerprinting framework.<n>It positions fingerprints near the intersection of all decision boundaries.<n>It constructs composite-sample fingerprints, each comprising multiple samples close to the multi-boundary intersection.<n>It assesses the discriminative power of generated fingerprints using statistical separability metrics.
• Score: 9.981615514164469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose IrisFP, a novel adversarial-example-based model fingerprinting framework that enhances both uniqueness and robustness by leveraging multi-boundary characteristics, multi-sample behaviors, and fingerprint discriminative power assessment to generate composite-sample fingerprints. Three key innovations make IrisFP outstanding: 1) It positions fingerprints near the intersection of all decision boundaries - unlike prior methods that target a single boundary - thus increasing the prediction margin without placing fingerprints deep inside target class regions, enhancing both robustness and uniqueness; 2) It constructs composite-sample fingerprints, each comprising multiple samples close to the multi-boundary intersection, to exploit collective behavior patterns and further boost uniqueness; and 3) It assesses the discriminative power of generated fingerprints using statistical separability metrics developed based on two reference model sets, respectively, for pirated and independently-trained models, retains the fingerprints with high discriminative power, and assigns fingerprint-specific thresholds to such retained fingerprints. Extensive experiments show that IrisFP consistently outperforms state-of-the-art methods, achieving reliable ownership verification by enhancing both robustness and uniqueness.

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