Talks

Jan 15, 2025 AdjointDEIS: Efficient Gradients for Diffusion Models
Mila – Quebec AI Institute
Abstract. Training-free guided generation is a powerful technique to exert additional control on the generative pipeline of diffusion models. We discuss the application of the continuous adjoint equations (sometimes called the adjoint method) on diffusion models. We then use these equations to estimate the gradients of the solution trajectory and conditional information with respect to some differentiable guiding function defined on the output. We discuss special optimizations we can make for Variance Preserving (VP) type diffusions to further simplify the continuous adjoint equations. We demonstrate an application of this technique as an adversarial attack against a Face Recognition (FR) system in the form of a face morphing attack, a powerful biometric attack which attempts to create a single image which successfully matches with multiple bona fide identities.
Nov 06, 2024 Diffusion Morphs (DiM): Diffusion is all you need for highly effective face morphs
Transatlantic Dialogue on Presentation Attack Detection – EAB and the iMARS project
Sep 18, 2024 Diffusion Morphs (DiM): Leveraging Diffusion for Strong and High-Quality Face Morphing Attacks
IEEE International Joint Conference on Biometrics – Journal Track
Abstract. Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a significant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image and the ability of the morphing attack to represent characteristics from both identities. We demonstrate the effectiveness of the proposed attack by evaluating its visual fidelity via the Frechet Inception Distance (FID). Also, extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The ability of a morphing attack detector to detect the proposed attack is measured and compared against two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. Additionally, a novel metric to measure the relative strength between different morphing attacks is introduced and evaluated.
Jul 23, 2024 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.