2024 07 Idiap

Diffusion Morphs (DiM): The Power of Iterative Generative Models for Attacking FR Systems
Biometrics Security & Privacy Group – Idiap Research Institute
Abstract. Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, DiMs suffer from slow inference speed, requiring a high number of Network Function Evaluations (NFE) and are still outperformed by landmark-based morphing attacks. In this talk I cover recent advancements in DiMs which address these issues. The talk will cover three recent advancements which are enumerated below: 1) Fast-DiM: The inference speed of DiMs are improved by employing higher-order numerical ODE solvers to reduce the number of NFE. 2) Greedy-DiM: The vulnerability of FR systems is dramatically increased by employing a greedy optimization strategy during each step of the generative process. Greedy-DiM beats landmark-based morphs on the studied dataset. 3) AdjointDEIS: A novel strategy for backprograting the gradients of diffusion models w.r.t. the initial noise, conditional information, and model parameters are presented for both probability flow ODE and diffusion SDE formulations of diffusion models using the method of adjoint sensitivity.