CPU-Auth is a novel authentication method that uses unique variations in a device’s CPU, measured via the Dynamic Voltage and Frequency Scaling (DVFS) governor from a browser, to create a hardware-based device fingerprint. The approach is evaluated on over 50,000 data traces using distance‑based and deep learning techniques. The study highlights how CPU-Auth could serve as a secondary authentication factor, addressing flaws in existing methods such as SMS, biometrics, and token‑based systems.
By Ryan Swift
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER). This paper introduces EERLoss: a subdifferentiable, arbitrarily accurate approximation to EER for training deep biometric models.
arXiv:2606. 24586v1 Announce Type: cross Abstract: Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER).
By Nahuel Gonzalez, Marta Robledo-Moreno, Ivan DeAndres-Tame, Ruben Vera-Rodriguez, Ruben Tolosana
arXiv:2606. 11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access.
By Kumar Kartikey, Nikos Komninos
The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.
By Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado
arXiv:2606. 21900v2 Announce Type: replace-cross Abstract: Continuous authentication for mobile and zero-trust systems requires nonintrusive evidence confirming the enrolled user-device context remains valid after initial login.
By Stergios Lantzos, Ilias Syrigos, Apostolos Apostolaras, Thanasis Korakis
The paper introduces BIND, a framework that binds a human’s biometric data to an AI agent’s identity and task-specific authority, enabling secure, real‑time delegation of control. By generating a token that an AI agent presents to an Identity Auditor, the system performs biometric authentication and recovers the agent’s ID and scope, providing non‑repudiable proof of human oversight. A practical implementation using face features and a fuzzy commitment scheme with turbo error‑correcting codes achieves a 96% true match rate at zero false match rate and supports 1024‑bit agent tokens.
By Joseph Geo Benjamin, Anil K Jain, Karthik Nandakumar
The paper presents a comprehensive benchmark for Domain Generalization (DG) in smartphone-based Human Activity Recognition (HAR), running over 410,000 experiments across multiple architectures, training objectives, initialization strategies, and architectural tweaks. It finds that individual DG components offer limited, highly conditional improvements, while combined configurations often yield stronger, sometimes super‑additive gains that depend on the model and shift scenario. The study also highlights that current source‑validation selection captures only a fraction of the potential oracle performance, underscoring the need for joint DG design and robust model‑selection methods.
By Ot\'avio Oliveira Napoli, Edson Borin
The paper reviews how voice authentication has evolved from handcrafted acoustic features to deep learning speaker embeddings, expanding its use in finance, smart devices, and law enforcement. It surveys modern threats—including data poisoning, adversarial, deepfake, and adversarial spoofing attacks—tracing their development alongside technological advances. For each attack type, the authors summarize methods, datasets, performance, and limitations, and organize the literature using accepted taxonomies to highlight emerging risks and open challenges.
By Kamel Kamel, Keshav Sood, Hridoy Sankar Dutta, Sunil Aryal
arXiv:2607. 00249v1 Announce Type: new Abstract: New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout.
By Geeling Chau, Ran Liu, Juri Minxha, Wenhui Cui, Erdrin Azemi, Ellen L. Zippi, Behrooz Mahasseni, Christopher M. Sandino
arXiv:2606. 25247v1 Announce Type: cross Abstract: Neural swipe decoders are typically tied to the keyboard they were trained on, requiring a new corpus and training run for each layout.
By David Lee Miller, Aleksandras Kostarevas
arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.
By Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe