DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis

• URL: http://arxiv.org/abs/2108.12715v1
• Date: Sat, 28 Aug 2021 22:56:09 GMT
• Title: DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis
• Authors: Kevin Lutz and Robert Bassett
• Abstract summary: We analyze an existing DeepFake detection technique based on head pose estimation. Our results correct the current literature's perception of state of the art performance for DeepFake detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area. In this paper, we analyze an existing DeepFake detection technique based on head pose estimation, which can be applied when fake images are generated with an autoencoder-based face swap. Existing literature suggests that this method is an effective DeepFake detector, and its motivating principles are attractively simple. With an eye towards using these principles to develop new DeepFake detectors, we conduct a reproducibility study of the existing method. We conclude that its merits are dramatically overstated, despite its celebrated status. By investigating this discrepancy we uncover a number of important and generalizable insights related to facial landmark detection, identity-agnostic head pose estimation, and algorithmic bias in DeepFake detectors. Our results correct the current literature's perception of state of the art performance for DeepFake detection.

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)
• Active Fake: DeepFake Camouflage [11.976015496109525]
  Face-Swap DeepFake fabricates behaviors by swapping original faces with synthesized ones. Existing forensic methods, primarily based on Deep Neural Networks (DNNs), effectively expose these manipulations and have become important authenticity indicators. We introduce a new framework for creating DeepFake camouflage that generates blending inconsistencies while ensuring imperceptibility, effectiveness, and transferability.
  arXiv  Detail & Related papers  (2024-09-05T02:46:36Z)
• 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)
• 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)
• 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)
• Countering Malicious DeepFakes: Survey, Battleground, and Horizon [17.153920019319603]
  The creation and the manipulation of facial appearance via deep generative approaches, known as DeepFake, have achieved significant progress. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of the battleground.
  arXiv  Detail & Related papers  (2021-02-27T13:48:54Z)
• Identity-Driven DeepFake Detection [91.0504621868628]
  Identity-Driven DeepFake Detection takes as input the suspect image/video as well as the target identity information. We output a decision on whether the identity in the suspect image/video is the same as the target identity. We present a simple identity-based detection algorithm called the OuterFace, which may serve as a baseline for further research.
  arXiv  Detail & Related papers  (2020-12-07T18:59:08Z)

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.