arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
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...
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
arXiv:2609.27011v1 Announce Type: new Abstract: Target-oriented face de-identification models aim to anonymize the identity of a target individual across different images or video frames, such that t...
arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.
The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images.
arXiv:2608. 03395v1 Announce Type: cross Abstract: Deepfake technologies pose increasing threats to facial privacy and identity security, motivating proactive defenses that protect facial images before misuse.
arXiv:2607. 29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition.
The paper introduces a "carrier"—a secondary visual element—to help preserve the main subject of an image during strong, unrestricted adversarial attacks. By allocating a larger portion of globally normalized attack updates to the carrier, the method reduces distortion of the subject while maintaining attack strength. Additionally, the carrier enhances cross-model transferability and allows targeted attacks to mislead classifiers while keeping the subject recognizable to humans.
Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave like backdoor-like triggers without being maliciously inserted.
arXiv:2607. 05516v2 Announce Type: replace-cross Abstract: Model-specific adversarial attacks have been extensively studied.
arXiv:2506.12454v2 Announce Type: replace-cross Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this wor...