FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection

• URL: http://arxiv.org/abs/2504.00454v1
• Date: Tue, 01 Apr 2025 06:19:50 GMT
• Title: FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection
• Authors: Yongze Li, Ning Li, Ajian Liu, Hui Ma, Liying Yang, Xihong Chen, Zhiyao Liang, Yanyan Liang, Jun Wan, Zhen Lei,
• Abstract summary: Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks.<n>Existing methods struggle to simultaneously detect physical and digital attacks due to significant intra-class variations.<n>We propose a unified attack detection model termed Frequency-Aware and Attack-Agnostic CLIP (FAsupertextscript3-CLIP)
• Score: 26.615096090240613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical and digital attacks due to: 1) significant intra-class variations between these attack types, and 2) the inadequacy of spatial information alone to comprehensively capture live and fake cues. To address these issues, we propose a unified attack detection model termed Frequency-Aware and Attack-Agnostic CLIP (FA\textsuperscript{3}-CLIP), which introduces attack-agnostic prompt learning to express generic live and fake cues derived from the fusion of spatial and frequency features, enabling unified detection of live faces and all categories of attacks. Specifically, the attack-agnostic prompt module generates generic live and fake prompts within the language branch to extract corresponding generic representations from both live and fake faces, guiding the model to learn a unified feature space for unified attack detection. Meanwhile, the module adaptively generates the live/fake conditional bias from the original spatial and frequency information to optimize the generic prompts accordingly, reducing the impact of intra-class variations. We further propose a dual-stream cues fusion framework in the vision branch, which leverages frequency information to complement subtle cues that are difficult to capture in the spatial domain. In addition, a frequency compression block is utilized in the frequency stream, which reduces redundancy in frequency features while preserving the diversity of crucial cues. We also establish new challenging protocols to facilitate unified face attack detection effectiveness. Experimental results demonstrate that the proposed method significantly improves performance in detecting physical and digital face attacks, achieving state-of-the-art results.

Related papers

• SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement [19.140558657697866]
  Face recognition systems are vulnerable to physical attacks and digital threats. The inherent differences among various attack types present significant challenges in identifying a common feature space. We propose SUEDE, the Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement.
  arXiv  Detail & Related papers  (2025-04-07T08:17:54Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection [96.539862328788]
  Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. We propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF) Experiment results on several public face forgery datasets demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T08:58:49Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Detecting Adversarial Faces Using Only Real Face Self-Perturbations [36.26178169550577]
  Adrial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples. Existing defense techniques achieve high accuracy in detecting some specific adversarial faces (adv-faces) New attack methods especially GAN-based attacks with completely different noise patterns circumvent them and reach a higher attack success rate.
  arXiv  Detail & Related papers  (2023-04-22T09:55:48Z)
• Exploring Frequency Adversarial Attacks for Face Forgery Detection [59.10415109589605]
  We propose a frequency adversarial attack method against face forgery detectors. Inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs attacks in both the spatial and frequency domains.
  arXiv  Detail & Related papers  (2022-03-29T15:34:13Z)
• MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection [11.124150983521158]
  We propose a novel framework named Multi-modal Contrastive Classification by Locally Correlated Representations. Our MC-LCR aims to amplify implicit local discrepancies between authentic and forged faces from both spatial and frequency domains. We achieve state-of-the-art performance and demonstrate the robustness and generalization of our method.
  arXiv  Detail & Related papers  (2021-10-07T09:24:12Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)

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.