arXiv:2607. 15512v1 Announce Type: cross Abstract: Attacks on general computer vision algorithms are often relegated to the digital domain, with the optimization performed purely in the digital world and then translated to physical mediums for implementation.
arXiv:2607. 29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition.
arXiv:2510. 25687v4 Announce Type: replace-cross Abstract: Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning (ML) models.
arXiv:2608.20884v1 Announce Type: new Abstract: Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport...
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.
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.
arXiv:2610.20278v1 Announce Type: new Abstract: Facial image watermarking can support traceability and certification in biometric pipelines, but provenance or manipulation detection alone does not gu...
arXiv:2608. 08521v1 Announce Type: cross Abstract: Face recognition systems face two distinct, commonly-separated failure modes: spoofing, where an impostor presents a photograph or video of an authorized user, and disguise, where a legitimate user is rejected because their appearance differs from their enrolled template due to accessories, facial hair, illumination, or pose.
arXiv:2602.02914v4 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) and face anonymization have different goals, but both must retain some identity-related information for...
FIDA (Feature Instability-Driven Attack) is a new backdoor attack framework targeting self‑supervised facial representation models. It employs subtle semantic triggers and a novel Feature Instability Loss that trains the encoder to heighten sensitivity of triggered features along perturbation directions, thereby avoiding the rigid feature patterns seen in prior attacks. Experiments demonstrate that FIDA achieves high attack success while largely preserving benign utility, exposing a significant threat to real‑world facial analysis applications.
arXiv:2607. 25926v1 Announce Type: cross Abstract: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition while preserving utility for downstream tasks.
arXiv:2609.27022v1 Announce Type: new Abstract: In this paper, we investigate the impact of adversarial attacks on identity encoders within a realistic de-identification framework. Our experiments sh...
The paper introduces Band-Attention Modulation Network (BAM‑Net), a face forgery detection framework that learns fine‑grained, adaptive modulation of frequency bands in the Discrete Cosine Transform spectrogram. BAM‑Net dynamically reweights anti‑diagonal frequency bands to enhance forgery‑related spectral cues while suppressing irrelevant information, then fuses this modulated frequency data with spatial features using a lightweight backbone with distance‑decayed attention. Experiments on FaceForensics++, Celeb‑DF, and DFDC show that BAM‑Net achieves state‑of‑the‑art performance and strong generalization across datasets, compression levels, and manipulation types.