Deep Learning Technology for Face Forgery Detection: A Survey

• URL: http://arxiv.org/abs/2409.14289v2
• Date: Tue, 24 Sep 2024 01:04:08 GMT
• Title: Deep Learning Technology for Face Forgery Detection: A Survey
• Authors: Lixia Ma, Puning Yang, Yuting Xu, Ziming Yang, Peipei Li, Huaibo Huang,
• Abstract summary: Deep learning has enabled the creation or manipulation of high-fidelity facial images and videos. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods.
• Score: 17.519617618071003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, the rapid development of computer vision and deep learning has enabled the creation or manipulation of high-fidelity facial images and videos via deep generative approaches. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. However, the technology can generate threats to personal privacy and national security by spreading misinformation. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods to distinguish fake faces from real faces. This paper presents a comprehensive survey of recent deep learning-based approaches for facial forgery detection. We attempt to provide the reader with a deeper understanding of the current advances as well as the major challenges for deepfake detection based on deep learning. We present an overview of deepfake techniques and analyse the characteristics of various deepfake datasets. We then provide a systematic review of different categories of deepfake detection and state-of-the-art deepfake detection methods. The drawbacks of existing detection methods are analyzed, and future research directions are discussed to address the challenges in improving both the performance and generalization of deepfake detection.

Related papers

• Deepfake detection in videos with multiple faces using geometric-fakeness features [79.16635054977068]
  Deepfakes of victims or public figures can be used by fraudsters for blackmailing, extorsion and financial fraud. In our research we propose to use geometric-fakeness features (GFF) that characterize a dynamic degree of a face presence in a video. We employ our approach to analyze videos with multiple faces that are simultaneously present in a video.
  arXiv  Detail & Related papers  (2024-10-10T13:10:34Z)
• Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
  AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity. Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
  arXiv  Detail & Related papers  (2024-08-01T08:57:47Z)
• Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
  Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions. This survey comprehensively reviews the latest developments in deepfake generation and detection. We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
  arXiv  Detail & Related papers  (2024-03-26T17:12:34Z)
• 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)
• GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning [63.547321642941974]
  We introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model. Experiment results reveal that our proposed GazeForensics outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-13T04:48:33Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• Deepfake Detection using Biological Features: A Survey [0.0]
  This study describes the history of deepfake, its development and detection, and the challenges based on physiological measurements. Deepfakes have been used to blackmail individuals, plan terrorist attacks, disseminate false information, defame individuals, and foment political turmoil.
  arXiv  Detail & Related papers  (2023-01-14T05:07:46Z)
• Using Deep Learning to Detecting Deepfakes [0.0]
  Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat.
  arXiv  Detail & Related papers  (2022-07-27T17:05:16Z)
• DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning [18.623444153774948]
  The study presents challenges, research trends, and directions related to DeepFake creation and detection techniques. The study reviews the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in the future.
  arXiv  Detail & Related papers  (2021-09-07T05:19:36Z)
• Understanding the Security of Deepfake Detection [23.118012417901078]
  We study the security of state-of-the-art deepfake detection methods in adversarial settings. We use two large-scale public deepfakes data sources including FaceForensics++ and Facebook Deepfake Detection Challenge. Our results uncover multiple security limitations of the deepfake detection methods in adversarial settings.
  arXiv  Detail & Related papers  (2021-07-05T14:18:21Z)
• Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward [2.15242029196761]
  It is possible to generate deepfakes to disseminate disinformation, revenge porn, financial frauds, hoaxes, and to disrupt government functioning. No attempt has been made to review approaches for detection and generation of both audio and video deepfakes. This paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation.
  arXiv  Detail & Related papers  (2021-02-25T18:26:50Z)

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.