GAN-generated Faces Detection: A Survey and New Perspectives

• URL: http://arxiv.org/abs/2202.07145v6
• Date: Thu, 9 Nov 2023 16:03:48 GMT
• Title: GAN-generated Faces Detection: A Survey and New Perspectives
• Authors: Xin Wang, Hui Guo, Shu Hu, Ming-Ching Chang, Siwei Lyu
• Abstract summary: Generative Adversarial Networks (GAN) have led to the generation of very realistic face images. The corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. We focus on methods that can detect face images that are generated or synthesized from GAN models.
• Score: 48.596997330548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.

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)
• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• Towards the Detection of AI-Synthesized Human Face Images [12.090322373964124]
  This paper presents a benchmark including human face images produced by Generative Adversarial Networks (GANs) and a variety of DMs. Then, the forgery traces introduced by different generative models have been analyzed in the frequency domain to draw various insights. The paper further demonstrates that a detector trained with frequency representation can generalize well to other unseen generative models.
  arXiv  Detail & Related papers  (2024-02-13T19:37:44Z)
• A survey on facial image deblurring [3.6775758132528877]
  When the facial image is blurred, it has a great impact on high-level vision tasks such as face recognition. This paper surveys and summarizes recently published methods for facial image deblurring, most of which are based on deep learning. We show the performance of classical methods on datasets and metrics and give a brief discussion on the differences of model-based and learning-based methods.
  arXiv  Detail & Related papers  (2023-02-10T02:24:56Z)
• Modeling biological face recognition with deep convolutional neural networks [0.0]
  Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Recent efforts have started to transfer this achievement to research on biological face recognition. In this review, we summarize the first studies that use DCNNs to model biological face recognition.
  arXiv  Detail & Related papers  (2022-08-13T16:45:30Z)
• Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues [81.15465149555864]
  We establish the first joint face spoofing and detection benchmark using both visual appearance and physiological r cues. To enhance the r periodicity discrimination, we design a two-branch physiological network using both facial powerful rtemporal signal map and its continuous wavelet transformed counterpart as inputs.
  arXiv  Detail & Related papers  (2022-08-10T15:41:48Z)
• Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces [40.15016121723183]
  We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models.
  arXiv  Detail & Related papers  (2021-09-01T03:25:50Z)
• DeepFake Detection Based on the Discrepancy Between the Face and its Context [94.47879216590813]
  We propose a method for detecting face swapping and other identity manipulations in single images. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context. We describe a method which uses the recognition signals from our two networks to detect such discrepancies. Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
  arXiv  Detail & Related papers  (2020-08-27T17:04:46Z)
• InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs [73.27299786083424]
  We propose a framework called InterFaceGAN to interpret the disentangled face representation learned by state-of-the-art GAN models. We first find that GANs learn various semantics in some linear subspaces of the latent space. We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection.
  arXiv  Detail & Related papers  (2020-05-18T18:01:22Z)
• DeepFake Detection by Analyzing Convolutional Traces [0.0]
  We focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
  arXiv  Detail & Related papers  (2020-04-22T09:02:55Z)

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.