Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes

• URL: http://arxiv.org/abs/2409.10889v1
• Date: Tue, 17 Sep 2024 04:58:30 GMT
• Title: Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes
• Authors: Zhixin Xie, Jun Luo,
• Abstract summary: Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been misused to produce deepfake videos for malicious purposes, including financial scams and political misinformation. We propose SFake, a new real-time deepfake detection method that exploits deepfake models' inability to adapt to physical interference.
• Score: 3.6308756891251392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been, very unfortunately, misused to produce deepfake videos (during web conferences, video calls, and identity authentication) for malicious purposes, including financial scams and political misinformation. Deepfake detection, as the countermeasure against deepfake, has attracted considerable attention from the academic community, yet existing works typically rely on learning passive features that may perform poorly beyond seen datasets. In this paper, we propose SFake, a new real-time deepfake detection method that innovatively exploits deepfake models' inability to adapt to physical interference. Specifically, SFake actively sends probes to trigger mechanical vibrations on the smartphone, resulting in the controllable feature on the footage. Consequently, SFake determines whether the face is swapped by deepfake based on the consistency of the facial area with the probe pattern. We implement SFake, evaluate its effectiveness on a self-built dataset, and compare it with six other detection methods. The results show that SFake outperforms other detection methods with higher detection accuracy, faster process speed, and lower memory consumption.

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)
• Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies [14.660707087391463]
  Deepfake videos present an increasing threat to society with potentially negative impact on criminal justice, democracy, and personal safety and privacy. We propose a novel unsupervised method for detecting deepfake videos by directly identifying intra-modal and cross-modal inconsistency between video segments. Our proposed method outperforms prior state-of-the-art unsupervised deepfake detection methods on the challenging FakeAVCeleb dataset.
  arXiv  Detail & Related papers  (2023-11-28T03:28:19Z)
• 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)
• DeePhy: On Deepfake Phylogeny [58.01631614114075]
  DeePhy is a novel Deepfake Phylogeny dataset which consists of 5040 deepfake videos generated using three different generation techniques. We present the benchmark on DeePhy dataset using six deepfake detection algorithms.
  arXiv  Detail & Related papers  (2022-09-19T15:30:33Z)
• Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption [94.5031244215761]
  We propose to boost the generalization of deepfake detection by distinguishing the "regularity disruption" that does not appear in real videos. Specifically, by carefully examining the spatial and temporal properties, we propose to disrupt a real video through a Pseudo-fake Generator. Such practice allows us to achieve deepfake detection without using fake videos and improves the generalization ability in a simple and efficient manner.
  arXiv  Detail & Related papers  (2022-07-21T10:42:34Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)
• 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)
• WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [82.42495493102805]
  We introduce a new dataset WildDeepfake which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet. We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically.
  arXiv  Detail & Related papers  (2021-01-05T11:10:32Z)
• Deepfake Detection using Spatiotemporal Convolutional Networks [0.0]
  Deepfake detection methods only use individual frames and therefore fail to learn from temporal information. We created a benchmark of performance using Celeb-DF dataset. Our methods outperformed state-of-theart frame-based detection methods.
  arXiv  Detail & Related papers  (2020-06-26T01:32:31Z)

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.