DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention

• URL: http://arxiv.org/abs/2309.06511v1
• Date: Tue, 12 Sep 2023 18:37:05 GMT
• Title: DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention
• Authors: Aaditya Kharel, Manas Paranjape, Aniket Bera
• Abstract summary: We present a novel multi-modal audio-video framework designed to concurrently process audio and video inputs for deepfake detection tasks. Our model capitalizes on lip synchronization with input audio through a cross-attention mechanism while extracting visual cues via a fine-tuned VGG-16 network.
• Score: 13.671150394943684
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the rise in manipulated media, deepfake detection has become an imperative task for preserving the authenticity of digital content. In this paper, we present a novel multi-modal audio-video framework designed to concurrently process audio and video inputs for deepfake detection tasks. Our model capitalizes on lip synchronization with input audio through a cross-attention mechanism while extracting visual cues via a fine-tuned VGG-16 network. Subsequently, a transformer encoder network is employed to perform facial self-attention. We conduct multiple ablation studies highlighting different strengths of our approach. Our multi-modal methodology outperforms state-of-the-art multi-modal deepfake detection techniques in terms of F-1 and per-video AUC scores.

Related papers

• AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies [8.01792778132834]
  AVT2-DWF aims to amplify both intra- and cross-modal forgery cues, thereby enhancing detection capabilities. AVT2-DWF adopts a dual-stage approach to capture both spatial characteristics and temporal dynamics of facial expressions. Experiments on DeepfakeTIMIT, FakeAVCeleb, and DFDC datasets indicate that AVT2-DWF achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-22T06:04:37Z)
• 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)
• Integrating Audio-Visual Features for Multimodal Deepfake Detection [33.51027054306748]
  Deepfakes are AI-generated media in which an image or video has been digitally modified. This paper proposes an audio-visual-based method for deepfake detection, which integrates fine-grained deepfake identification with binary classification.
  arXiv  Detail & Related papers  (2023-10-05T18:19:56Z)
• MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection [4.659427498118277]
  A novel kind of deepfakes has emerged with either audio or visual modalities manipulated. Existing multimodal deepfake detectors are often based on the fusion of the audio and visual streams from the video. In this paper, we tackle the problem at the representation level to aid the fusion of audio and visual streams for multimodal deepfake detection.
  arXiv  Detail & Related papers  (2023-10-03T17:43:24Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• 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)
• Zorro: the masked multimodal transformer [68.99684436029884]
  Zorro is a technique that uses masks to control how inputs from each modality are routed inside Transformers. We show that with contrastive pre-training Zorro achieves state-of-the-art results on most relevant benchmarks for multimodal tasks.
  arXiv  Detail & Related papers  (2023-01-23T17:51:39Z)
• M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
  forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information. In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection. We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
  arXiv  Detail & Related papers  (2021-04-20T05:43:44Z)
• Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues [75.1731999380562]
  We present a learning-based method for detecting real and fake deepfake multimedia content. We extract and analyze the similarity between the two audio and visual modalities from within the same video. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets.
  arXiv  Detail & Related papers  (2020-03-14T22:07:26Z)

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.