Media Forensics and Deepfake Systematic Survey

• URL: http://arxiv.org/abs/2406.13295v1
• Date: Wed, 19 Jun 2024 07:33:33 GMT
• Title: Media Forensics and Deepfake Systematic Survey
• Authors: Nadeem Jabbar CH, Aqib Saghir, Ayaz Ahmad Meer, Salman Ahmad Sahi, Bilal Hassan, Siddiqui Muhammad Yasir,
• Abstract summary: Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way. It can be used to make movies look better as well as to spread false information by imitating famous people.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way making it hard to differentiate the real from the fake features It can be used to make movies look better as well as to spread false information by imitating famous people In this paper many different ways to make a Deepfake are explained analyzed and separated categorically Using Deepfake datasets models are trained and tested for reliability through experiments Deepfakes are a type of facial manipulation that allow people to change their entire faces identities attributes and expressions The trends in the available Deepfake datasets are also discussed with a focus on how they have changed Using Deep learning a general Deepfake detection model is made Moreover the problems in making and detecting Deepfakes are also mentioned As a result of this survey it is expected that the development of new Deepfake based imaging tools will speed up in the future This survey gives indepth review of methods for manipulating images of face and various techniques to spot altered face images Four types of facial manipulation are specifically discussed which are attribute manipulation expression swap entire face synthesis and identity swap Across every manipulation category we yield information on manipulation techniques significant benchmarks for technical evaluation of counterfeit detection techniques available public databases and a summary of the outcomes of all such analyses From all of the topics in the survey we focus on the most recent development of Deepfake showing its advances and obstacles in detecting fake images

Related papers

• DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion [94.46904504076124]
  Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. We introduce DiffusionFake, a novel framework that reverses the generative process of face forgeries to enhance the generalization of detection models.
  arXiv  Detail & Related papers  (2024-10-06T06:22:43Z)
• Deep Learning Technology for Face Forgery Detection: A Survey [17.519617618071003]
  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.
  arXiv  Detail & Related papers  (2024-09-22T01:42:01Z)
• Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach [77.65459419417533]
  We propose an automatic dataset expansion technique to support semantics-oriented DeepFake detection tasks. We also resort to joint embedding of face images and their corresponding labels for prediction. Our method improves the generalizability of DeepFake detection and renders some degree of model interpretation by providing human-understandable explanations.
  arXiv  Detail & Related papers  (2024-08-29T07:11:50Z)
• 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)
• 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)
• Deepfake detection by exploiting surface anomalies: the SurFake approach [29.088218634944116]
  This paper investigates how deepfake creation can impact on the characteristics that the whole scene had at the time of the acquisition. By resorting to the analysis of the characteristics of the surfaces depicted in the image it is possible to obtain a descriptor usable to train a CNN for deepfake detection.
  arXiv  Detail & Related papers  (2023-10-31T16:54:14Z)
• Real Face Foundation Representation Learning for Generalized Deepfake Detection [74.4691295738097]
  The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is almost impossible to collect sufficient representative fake faces, and it is hard for existing detectors to generalize to all types of manipulation. We propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets.
  arXiv  Detail & Related papers  (2023-03-15T08:27:56Z)
• Robust Face-Swap Detection Based on 3D Facial Shape Information [59.32489266682952]
  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.
  arXiv  Detail & Related papers  (2021-04-28T09:35:48Z)
• 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)
• DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection [17.602598143822917]
  This survey provides a review of techniques for manipulating face images including DeepFake methods. In particular, four types of facial manipulation are reviewed: entire face synthesis, identity swap (DeepFakes), attribute manipulation, and expression swap. We pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection.
  arXiv  Detail & Related papers  (2020-01-01T09:54:34Z)

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.