DiffMI recovers face identities from embeddings using a diffusion-driven training-free pipeline with latent initialization, ranked adversarial refinement, and confidence-aware optimization, achieving 84-93% success on resilient models.
Joint face detection and alignment using multitask cascaded convolutional networks,
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The paper introduces a continual learning framework combining synthetic sketch generation and trusted sample replay to enable a single model to perform multiple sketch biometric identification tasks.
FaceCloak learns a lightweight identity-specific cloaking mask from a single image via synthetic face generation and iterative embedding perturbation to evade multiple recognition models.
citing papers explorer
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DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion
DiffMI recovers face identities from embeddings using a diffusion-driven training-free pipeline with latent initialization, ranked adversarial refinement, and confidence-aware optimization, achieving 84-93% success on resilient models.
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Bridging Data Trials and Task Barriers: A Unified Framework for Sketch Biometric Identification
The paper introduces a continual learning framework combining synthetic sketch generation and trusted sample replay to enable a single model to perform multiple sketch biometric identification tasks.
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Personalized Face Privacy Protection From a Single Image
FaceCloak learns a lightweight identity-specific cloaking mask from a single image via synthetic face generation and iterative embedding perturbation to evade multiple recognition models.