Structure Destruction and Content Combination for Face Anti-Spoofing

• URL: http://arxiv.org/abs/2107.10628v1
• Date: Thu, 22 Jul 2021 13:08:46 GMT
• Title: Structure Destruction and Content Combination for Face Anti-Spoofing
• Abstract summary: Anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types. These methods are prone to stick on memorization of the entire training dataset and show sensitivity to nonhomologous domain distribution. We propose Structure Destruction Module and Content Combination Module to address these two imitations.
• Score: 14.413900076141093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision. However, limited by the following two inherent disturbances in their training process: 1) Complete facial structure in a single image. 2) Implicit subdomains in the whole dataset, these methods are prone to stick on memorization of the entire training dataset and show sensitivity to nonhomologous domain distribution. In this paper, we propose Structure Destruction Module and Content Combination Module to address these two imitations separately. The former mechanism destroys images into patches to construct a non-structural input, while the latter mechanism recombines patches from different subdomains or classes into a mixup construct. Based on this splitting-and-splicing operation, Local Relation Modeling Module is further proposed to model the second-order relationship between patches. We evaluate our method on extensive public datasets and promising experimental results to demonstrate the reliability of our method against state-of-the-art competitors.

Related papers

• Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing [59.765442274924595]
  We propose a compositional visual prompt learning framework that operates entirely in the visual feature space.<n>The framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units.<n>Experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
  arXiv  Detail & Related papers  (2026-08-18T04:18:08Z)
• SegDem: Segmentation helps Demosaicing [52.515779223891705]
  Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter (CFA)<n>Most existing methods formulate demosaicing as pixel-level reconstruction.<n>SegDem is a cross-task decoder representation transfer framework for demosaicing.
  arXiv  Detail & Related papers  (2026-08-08T04:49:20Z)
• Combined Flicker-banding and Moire Removal for Screen-Captured Images [24.036188551666573]
  We present the first systematic study on joint removal of moiré patterns and flicker-banding in screen-captured images.<n>To support this task, we construct a large-scale dataset containing both moiré patterns and flicker-banding.<n>We also introduce an ISP-based flicker simulation pipeline to stabilize model training and expand the degradation distribution.
  arXiv  Detail & Related papers  (2026-02-02T02:53:41Z)
• VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference [53.31458914370742]
  VizDefender is a framework for tampering detection and analysis.<n>The framework integrates two core components: 1) a semi-fragile watermark module that protects the visualization by embedding a location map to images, and 2) an intent analysis module that leverages Multimodal Large Language Models (MLLMs) to interpret manipulation.
  arXiv  Detail & Related papers  (2025-12-21T18:44:03Z)
• Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations [56.816929931908824]
  We pioneer the detection of semantically-coordinated manipulations in multimodal data.<n>We propose a Retrieval-Augmented Manipulation Detection and Grounding (RamDG) framework.<n>Our framework significantly outperforms existing methods, achieving 2.06% higher detection accuracy on SAMM compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-16T04:18:48Z)
• Active Adversarial Noise Suppression for Image Forgery Localization [56.98050814363447]
  We introduce an Adversarial Noise Suppression Module (ANSM) that generate a defensive perturbation to suppress the attack effect of adversarial noise.<n>To our best knowledge, this is the first report of adversarial defense in image forgery localization tasks.
  arXiv  Detail & Related papers  (2025-06-15T14:53:27Z)
• Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction [50.952228546326516]
  Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures and occlusions. Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts. We propose a novel framework that attempts to precisely align hand poses and interactions by integrating foundation model-driven 2D priors with diffusion-based interaction refinement.
  arXiv  Detail & Related papers  (2025-03-22T14:42:27Z)
• Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection [68.74469657656822]
  Unsupervised anomaly detection (AD) aims to train robust detection models using only normal samples. Recent research focuses on a unified unsupervised AD setting in which only one model is trained for all classes. We introduce a novel Reconstruction as Sequence (RAS) method, which enhances the contextual correspondence during feature reconstruction.
  arXiv  Detail & Related papers  (2024-09-10T07:37:58Z)
• Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal [5.083330121710086]
  GAN-based training often faces issues such as mode collapse and unstable optimization. We propose a semantic-guided adversarial diffusion framework for self-supervised shadow removal. We conduct experiments on multiple public datasets, and the experimental results demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T09:14:38Z)
• Rethinking and Defending Protective Perturbation in Personalized Diffusion Models [21.30373461975769]
  We study the fine-tuning process of personalized diffusion models (PDMs) through the lens of shortcut learning. PDMs are susceptible to minor adversarial perturbations, leading to significant degradation when fine-tuned on corrupted datasets. We propose a systematic defense framework that includes data purification and contrastive decoupling learning.
  arXiv  Detail & Related papers  (2024-06-27T07:14:14Z)
• Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration [64.84134880709625]
  We show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss. We present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model.
  arXiv  Detail & Related papers  (2024-06-26T17:40:30Z)
• SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images [85.52629779976137]
  We propose a structure-aware Mixup scheme to paste road regions from one image onto another for creating an image scene with increased complexity. A discriminator-based regularization is designed for enhancing the connectivity meanwhile preserving the structure of roads. Our framework demonstrates superior performance on the DeepGlobe, Wuhan, and Massachusetts datasets.
  arXiv  Detail & Related papers  (2024-03-03T02:56:43Z)
• Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration [50.62725807357586]
  This article presents a general Bayesian learning framework for multi-modal groupwise image registration. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables. Experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images.
  arXiv  Detail & Related papers  (2024-01-04T08:46:39Z)
• Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement [13.442093381065268]
  We learn a general add-on structural feature extractor, by explicitly enforcing the structural alignment between an input and its synthesized image. Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities. We train, validate, and test our network on 1,768, 416, and 1,560 unpaired subject-independent slices of tagged and cine magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-02-25T03:09:46Z)
• Self-supervised Correlation Mining Network for Person Image Generation [9.505343361614928]
  Person image generation aims to perform non-rigid deformation on source images. We propose a Self-supervised Correlation Mining Network (SCM-Net) to rearrange the source images in the feature space. For improving the fidelity of cross-scale pose transformation, we propose a graph based Body Structure Retaining Loss.
  arXiv  Detail & Related papers  (2021-11-26T03:57:46Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Disassembling Object Representations without Labels [75.2215716328001]
  We study a new representation-learning task, which we termed as disassembling object representations. Disassembling enables category-specific modularity in the learned representations. We propose an unsupervised approach to achieving disassembling, named Unsupervised Disassembling Object Representation (UDOR)
  arXiv  Detail & Related papers  (2020-04-03T08:23:09Z)
• Learning Invariant Representation for Unsupervised Image Restoration [20.61038510024114]
  Cross domain transfer has been applied for unsupervised image restoration tasks. We propose an unsupervised learning method that explicitly learns invariant presentation from noisy data.
  arXiv  Detail & Related papers  (2020-03-28T11:20:21Z)

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.