Protego: User-Centric Pose-Invariant Privacy Protection Against Face Recognition-Induced Digital Footprint Exposure
Face recognition (FR) technologies are increasingly used to power large-scale image retrieval systems, raising serious privacy concerns. Services like Clearview AI and PimEyes allow anyone to upload a facial photo and retrieve a large amount of online content associated with that person. This not only enables identity inference but also exposes their digital footprint, such as social media activity, private photos, and news reports, often without their consent. In response to this emerging threat, we propose Protego, a user-centric privacy protection method that safeguards facial images from such retrieval-based privacy intrusions. Protego encapsulates a user's 3D facial signatures into a pose-invariant 2D representation, which is dynamically deformed into a natural-looking 3D mask tailored to the pose and expression of any facial image of the user, and applied prior to online sharing. Motivated by a critical limitation of existing methods, Protego amplifies the sensitivity of FR models so that protected images cannot be matched even among themselves. Experiments show that Protego significantly reduces retrieval accuracy across a wide range of black-box FR models and performs at least 2x better than existing methods. It also offers unprecedented visual coherence, particularly in video settings where consistency and natural appearance are essential. Overall, Protego contributes to the fight against the misuse of FR for mass surveillance and unsolicited identity tracing.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionImage RetrievalSimilar Papers 제목 키워드 기반
Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities
Transformer models have excelled in natural language tasks, prompting the vision community to explore their implementation in computer vision problems. However, these models are still influenced by adversarial examples. …
Adversarial AttackDETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning
Aligning egocentric video with wearable sensors have shown promise for human action recognition, but face practical limitations in user discomfort, privacy concerns, and scalability. We explore exocentric video with ambi…
Action RecognitionOnline ClusteringPEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning
In the evolving landscape of human-centric systems, personalized privacy solutions are becoming increasingly crucial due to the dynamic nature of human interactions. Traditional static privacy models often fail to meet t…
Reinforcement Learning (RL)PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing
Recent advances in generative image editing have enabled transformative applications, from professional head shot generation to avatar stylization. However, these systems often require uploading high-fidelity facial imag…
Image EditingSurfacing Privacy Settings Using Semantic Matching
Online services utilize privacy settings to provide users with control over their data. However, these privacy settings are often hard to locate, causing the user to rely on provider-chosen default values. In this work, …
Semantic SimilaritySemantic Textual Similarity