Robust Face-Swap Detection Based on 3D Facial Shape Information

• URL: http://arxiv.org/abs/2104.13665v1
• Date: Wed, 28 Apr 2021 09:35:48 GMT
• Title: Robust Face-Swap Detection Based on 3D Facial Shape Information
• Authors: Weinan Guan, Wei Wang, Jing Dong, Bo Peng and Tieniu Tan
• Abstract summary: Face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues. We propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures.
• Score: 59.32489266682952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues. Therefore, these approaches show weak interpretability and robustness. In this paper, we propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures. The key aspect of our method is obtaining the inconsistency of 3D facial shape and facial appearance, and the inconsistency based clue offers natural interpretability for the proposed face-swap detection method. Experimental results show the superiority of our method in robustness on various laundering and cross-domain data, which validates the effectiveness of the proposed method.

Related papers

• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• 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)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Building an Invisible Shield for Your Portrait against Deepfakes [34.65356811439098]
  We propose a novel framework - Integrity Encryptor, aiming to protect portraits in a proactive strategy. Our methodology involves covertly encoding messages that are closely associated with key facial attributes into authentic images. The modified facial attributes serve as a mean of detecting manipulated images through a comparison of the decoded messages.
  arXiv  Detail & Related papers  (2023-05-22T10:01:28Z)
• Detect and Locate: A Face Anti-Manipulation Approach with Semantic and Noise-level Supervision [67.73180660609844]
  We propose a conceptually simple but effective method to efficiently detect forged faces in an image. The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image. The proposed model achieves state-of-the-art detection accuracy and remarkable localization performance.
  arXiv  Detail & Related papers  (2021-07-13T02:59:31Z)
• Face Forgery Detection by 3D Decomposition [72.22610063489248]
  We consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment. By disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. We propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns.
  arXiv  Detail & Related papers  (2020-11-19T09:25:44Z)
• 3D Face Anti-spoofing with Factorized Bilinear Coding [35.30886962572515]
  We propose a novel anti-spoofing method from the perspective of fine-grained classification. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection.
  arXiv  Detail & Related papers  (2020-05-12T03:09:20Z)
• One-Shot GAN Generated Fake Face Detection [3.3707422585608953]
  We propose a universal One-Shot GAN generated fake face detection method. The proposed method is based on extracting out-of-context objects from faces via scene understanding models. Our experiments show that, we can discriminate fake faces from real ones in terms of out-of-context features.
  arXiv  Detail & Related papers  (2020-03-27T05:51:14Z)
• Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces [32.391300491317445]
  We equip our method with the aggregation of manipulated face images. Small but carefully crafted geometric manipulation in the input domain can fool deep face recognition models. Our approach is demonstrated its superiority compared to the state-of-the-art method on benchmark datasets AFLW, 300-W, and COFW.
  arXiv  Detail & Related papers  (2020-01-07T16:43:09Z)

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.