Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection

• URL: http://arxiv.org/abs/2210.14153v1
• Date: Fri, 21 Oct 2022 23:31:17 GMT
• Title: Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection
• Authors: Hui Guo, Xin Wang, Siwei Lyu
• Abstract summary: We describe a new active forensic method to detect real-time DeepFakes. We authenticate video calls by displaying a distinct pattern on the screen and using the corneal reflection extracted from the images of the call participant's face. This pattern can be induced by a call participant displaying on a shared screen or directly integrated into the video-call client.
• Score: 43.272069005626584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The COVID pandemic has led to the wide adoption of online video calls in recent years. However, the increasing reliance on video calls provides opportunities for new impersonation attacks by fraudsters using the advanced real-time DeepFakes. Real-time DeepFakes pose new challenges to detection methods, which have to run in real-time as a video call is ongoing. In this paper, we describe a new active forensic method to detect real-time DeepFakes. Specifically, we authenticate video calls by displaying a distinct pattern on the screen and using the corneal reflection extracted from the images of the call participant's face. This pattern can be induced by a call participant displaying on a shared screen or directly integrated into the video-call client. In either case, no specialized imaging or lighting hardware is required. Through large-scale simulations, we evaluate the reliability of this approach under a range in a variety of real-world imaging scenarios.

Related papers

• AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection [32.502184301996216]
  Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection. This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor.
  arXiv  Detail & Related papers  (2023-11-05T18:35:03Z)
• AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection [53.448283629898214]
  The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality. We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)
• NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection [50.33525966541906]
  Existing multimodal detection methods capture audio-visual inconsistencies to expose Deepfake videos. We propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Our model can be easily adapted to the downstream Deepfake datasets with fine-tuning.
  arXiv  Detail & Related papers  (2023-06-12T06:06:05Z)
• Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video [41.4300990443683]
  Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. We shed light on this research field for the first time with essential contributions on dataset, theory, and practices.
  arXiv  Detail & Related papers  (2023-03-28T15:35:25Z)
• 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)
• Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection [112.96004727646115]
  We develop a method to detect face-manipulated videos using real talking faces. We show that our method achieves state-of-the-art performance on cross-manipulation generalisation and robustness experiments. Our results suggest that leveraging natural and unlabelled videos is a promising direction for the development of more robust face forgery detectors.
  arXiv  Detail & Related papers  (2022-01-18T17:14:54Z)
• Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion [82.06128362686445]
  We propose a multi-modal semantic forensic approach to handle both cheapfakes and visually persuasive deepfakes. We leverage the idea of attribution to learn person-specific biometric patterns that distinguish a given speaker from others. Unlike existing person-specific approaches, our method is also effective against attacks that focus on lip manipulation.
  arXiv  Detail & Related papers  (2021-12-21T01:57:04Z)
• FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios [9.10316289334594]
  This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep learning based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conferencing based meeting.
  arXiv  Detail & Related papers  (2021-01-09T09:06:08Z)
• Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing [9.435020319411311]
  Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack. Previous works have shown the effectiveness of using depth and temporal supervision for this task. We propose a novel two stream FreqSaptialTemporalNet for face anti-spoofing which simultaneously takes advantage of frequent, spatial and temporal information.
  arXiv  Detail & Related papers  (2020-01-20T06:02:45Z)

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.