Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection

• URL: http://arxiv.org/abs/2307.11438v1
• Date: Fri, 21 Jul 2023 08:58:49 GMT
• Title: Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection
• Authors: Decheng Liu, Tao Chen, Chunlei Peng, Nannan Wang, Ruimin Hu, Xinbo Gao
• Abstract summary: 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.
• Score: 96.539862328788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. In this paper, we propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF). Most forgery technologies always bring in high-frequency aware cues, which make it easy to distinguish source authenticity but difficult to generalize to unseen artifact types. The masked frequency forgery representation module is designed to explore robust forgery cues by randomly discarding high-frequency information. In addition, we find that the forgery attention map inconsistency through the detection network could affect the generalizability. Thus, the forgery attention consistency is introduced to force detectors to focus on similar attention regions for better generalization ability. Experiment results on several public face forgery datasets (FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.

Related papers

• 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)
• Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues [61.37306431455152]
  We propose a novel Hierarchical Forgery for Multi-modality Face Forgery Detection (HFC-MFFD) The HFC-MFFD learns robust patches-based hybrid representation to enhance forgery authentication in multiple-modality scenarios. The specific hierarchical face forgery is proposed to alleviate the class imbalance problem and further boost detection performance.
  arXiv  Detail & Related papers  (2022-12-30T10:54:29Z)
• SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes [7.553507857251396]
  We propose a novel deepfake detector, called SeeABLE, that formalizes the detection problem as a (one-class) out-of-distribution detection task. SeeABLE pushes perturbed faces towards predefined prototypes using a novel regression-based bounded contrastive loss. We show that our model convincingly outperforms competing state-of-the-art detectors, while exhibiting highly encouraging generalization capabilities.
  arXiv  Detail & Related papers  (2022-11-21T09:38:30Z)
• FedForgery: Generalized Face Forgery Detection with Residual Federated Learning [87.746829550726]
  Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training. The paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery) Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.
  arXiv  Detail & Related papers  (2022-10-18T03:32:18Z)
• 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)
• Local Relation Learning for Face Forgery Detection [73.73130683091154]
  We propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions. We also propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation.
  arXiv  Detail & Related papers  (2021-05-06T10:44:32Z)

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.