Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2605.13108v1
• Date: Wed, 13 May 2026 07:19:28 GMT
• Title: Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection
• Abstract summary: Motion cues are highly discriminative for FacePAD but typically require explicit optical flow estimation.<n>We propose a dual-branch teacher model that fuses appearance cues from RGB frames with motion cues derived from colorwheel-encoded optical flow.
• Score: 8.286841244758845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face presentation attack detection (FacePAD) remains challenging under diverse spoofing representation, including 2D print and replay, 3D mask-based spoofing, makeup-induced appearance manipulation, and physical occlusions, as well as under varying capture conditions. Motion cues are highly discriminative for FacePAD but typically require explicit optical flow estimation, which introduces substantial computational overhead and limits real-time deployment. In this work, we leverage optical flow to enhance motion representation during training while eliminating the need for flow computation at inference. We propose a dual-branch teacher model that fuses appearance cues from RGB frames with motion cues derived from colorwheel-encoded optical flow, enabling effective modeling of micro-motions and temporal consistency. To enable efficient deployment, we introduce a knowledge distillation framework that transfers motion-aware knowledge from the flow-augmented teacher to a lightweight RGB-only student via logit distillation. As a result, the student implicitly learns motion-sensitive representations without requiring explicit flow estimation or additional feature extraction blocks at inference. Extensive experiments demonstrate strong performance across multiple benchmarks, achieving 0.0% HTER on Replay-Attack and Replay-Mobile, 0.94% HTER on ROSE-Youtu, 5.65% HTER on SiW-Mv2, and 0.42% ACER on OULU-NPU. The distilled student achieves performance comparable to or better than the teacher while significantly reducing parameters and FLOPs, achieving 52 FPS on an NVIDIA Jetson Orin Nano, indicating its suitability for real-time and resource-constrained FacePAD deployment.

Related papers

• CAKE: Real-time Action Detection via Motion Distillation and Background-aware Contrastive Learning [3.1978938942105644]
  Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of temporal dynamics against background motion.<n>We propose CAKE, a OAD Flow-based distillation framework to transfer motion knowledge into RGB models.<n>Our model operates at over 72 FPS on a single CPU, making it highly suitable for resource-constrained systems.
  arXiv  Detail & Related papers  (2026-03-25T06:37:12Z)
• IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation [58.297199313494]
  Implicit methods capture motion semantics directly from driving video, but suffer from identity leakage and entanglement between motion and appearance.<n>We propose a novel implicit motion representation that compresses per-frame motion into compact 1D motion tokens.<n>Our methodology employs a three-stage training strategy to enhance the training efficiency and ensure high fidelity.
  arXiv  Detail & Related papers  (2026-02-07T11:17:20Z)
• Robotic VLA Benefits from Joint Learning with Motion Image Diffusion [114.60268819583017]
  Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions.<n>We propose joint learning with motion image diffusion, a novel strategy that enhances VLA models with motion reasoning capabilities.<n> Experiments in both simulation and real-world environments demonstrate that joint learning with motion image diffusion improves the success rate of pi-series VLAs to 97.5%.
  arXiv  Detail & Related papers  (2025-12-19T19:07:53Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation [6.856317526681759]
  Visual place recognition plays a pivotal role in autonomous exploration and navigation of mobile robots.<n>Existing methods overcome this by exploiting powerful yet large networks.<n>We propose a high-performance teacher and lightweight student distillation framework called TSCM.
  arXiv  Detail & Related papers  (2024-04-02T02:29:41Z)
• Motion Sensitive Contrastive Learning for Self-supervised Video Representation [34.854431881562576]
  Motion Sensitive Contrastive Learning (MSCL) injects the motion information captured by optical flows into RGB frames to strengthen feature learning. Local Motion Contrastive Learning (LMCL) with frame-level contrastive objectives across the two modalities. Flow Rotation Augmentation (FRA) to generate extra motion-shuffled negative samples and Motion Differential Sampling (MDS) to accurately screen training samples.
  arXiv  Detail & Related papers  (2022-08-12T04:06:56Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• Unsupervised Motion Representation Enhanced Network for Action Recognition [4.42249337449125]
  Motion representation between consecutive frames has proven to have great promotion to video understanding. TV-L1 method, an effective optical flow solver, is time-consuming and expensive in storage for caching the extracted optical flow. We propose UF-TSN, a novel end-to-end action recognition approach enhanced with an embedded lightweight unsupervised optical flow estimator.
  arXiv  Detail & Related papers  (2021-03-05T04:14:32Z)
• Reinforcement Learning with Latent Flow [78.74671595139613]
  Flow of Latents for Reinforcement Learning (Flare) is a network architecture for RL that explicitly encodes temporal information through latent vector differences. We show that Flare recovers optimal performance in state-based RL without explicit access to the state velocity. We also show that Flare achieves state-of-the-art performance on pixel-based challenging continuous control tasks within the DeepMind control benchmark suite.
  arXiv  Detail & Related papers  (2021-01-06T03:50:50Z)
• PAN: Towards Fast Action Recognition via Learning Persistence of Appearance [60.75488333935592]
  Most state-of-the-art methods heavily rely on dense optical flow as motion representation. In this paper, we shed light on fast action recognition by lifting the reliance on optical flow. We design a novel motion cue called Persistence of Appearance (PA) In contrast to optical flow, our PA focuses more on distilling the motion information at boundaries.
  arXiv  Detail & Related papers  (2020-08-08T07:09:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.