In recent years, palmprints have been extensively utilized for individual verification. The abundance of sensitive information in palmprint data necessitates robust protection to ensure security and privacy without compromising system performance. Existing systems frequently use cancelable transformations to protect palmprint templates. However, if an adversary gains access to the stored database, they could initiate a replay attack before the system detects the breach and can revoke and replace the reference template. To address replay attacks while meeting template protection criteria, we propose a dual-level cancelable palmprint verification framework. In this framework, the reference template is initially transformed using a cancelable competition hashing network with a first-level token, enabling the end-to-end generation of cancelable templates. During enrollment, the system creates a negative database (NDB) using a second-level token for further protection. Due to the unique NDB-to-vector matching characteristic, a replay attack involving the matching between the reference template and a compromised instance in NDB form is infeasible. This approach effectively addresses the replay attack problem at its root. Furthermore, the dual-level protected reference template enjoys heightened security, as reversing the NDB is NP-hard. We also propose a novel NDB-to-vector matching algorithm based on matrix operations to expedite the matching process, addressing the inefficiencies of previous NDB methods reliant on dictionary-based matching rules. Extensive experiments conducted on public palmprint datasets confirm the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://github.com/Zi-YuanYang/DCPV .

中文翻译：

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用于掌纹验证和防黑客数据存储的双层可取消框架


近年来，掌纹被广泛用于个人验证。掌纹数据中包含大量敏感信息，因此需要强有力的保护，以确保安全和隐私，同时又不影响系统性能。现有系统经常使用可取消的转换来保护掌纹模板。然而，如果对手获得了对存储数据库的访问权限，他们就可以在系统检测到违规之前发起重放攻击，并可以撤销和替换参考模板。为了在满足模板保护标准的同时解决重放攻击，我们提出了一种双层可取消掌纹验证框架。在此框架中，参考模板最初使用具有第一级令牌的可取消竞争哈希网络进行转换，从而实现可取消模板的端到端生成。在注册过程中，系统使用二级令牌创建一个负数据库（NDB）以进行进一步保护。由于独特的 NDB 到向量匹配特性，涉及参考模板与 NDB 形式的受感染实例之间的匹配的重放攻击是不可行的。这种方法有效地从根本上解决了重放攻击问题。此外，双层保护参考模板具有更高的安全性，因为反转 NDB 是 NP 难的。我们还提出了一种基于矩阵运算的新型 NDB 到向量匹配算法，以加快匹配过程，解决了先前依赖于基于字典的匹配规则的 NDB 方法的低效率问题。在公共掌纹数据集上进行的广泛实验证实了所提出框架的有效性和通用性。论文被接受后，代码将可在 https://github.com/Zi-YuanYang/DCPV 访问。