Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

• URL: http://arxiv.org/abs/2606.15117v1
• Date: Sat, 13 Jun 2026 05:11:15 GMT
• Title: Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection
• Abstract summary: We propose a generalized deep ensemble audio-visual model (EAV-DFD) combined with a domain adaptation mechanism.<n>Our experimental results demonstrate that the proposed framework is efficient in domain adaptation.<n>This leads to a novel deepfake detection model capable of adapting to new domains and interpreting which modality has been manipulated.
• Score: 6.39593002676344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both. This raises severe privacy and societal concerns. Numerous studies in this area have yielded promising intra-domain results; however, these models frequently exhibit decreased efficacy when faced with data from dissimilar domains. Consequently, recent deepfake detection approaches focus on enhancing the generalization ability through multiple techniques that incorporate all input modalities, including audio, images, and their interactions. In this regard, we propose the EAV-DFD method, a generalized deep ensemble audio-visual model (EAV-DFD) combined with a domain adaptation mechanism utilizing a teacher-student framework to enhance the model's ability to perform and generalize effectively across unseen domains. To evaluate the model's performance, we used the FakeAVCeleb dataset as the primary domain and the DFDC, Deepfake_TIMIT, and PolyGlotFake datasets as an unseen domain. Our experimental results demonstrate that the proposed framework is efficient in domain adaptation, improving AUC performance of the model by 4.09%, 17.94%, and 0.5% on three unseen datasets, using only a small portion of them to train the student model. This leads to a novel deepfake detection model capable of adapting to new domains and interpreting which modality has been manipulated, highlighting the potential of our approach for real-world applications.

Related papers

• Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector [0.0]
  Diffusion-based generative models have shown remarkable abilities in generating high-quality and diverse images.<n>We train a detector with frozen-weight diffusion model on the source domain, then employ it as a teacher model to generate pseudo labels on the unlabeled target domain.<n>Our method achieves an average mAP improvement of 21.2% compared to the baseline on 6 datasets.
  arXiv  Detail & Related papers  (2025-06-04T17:56:46Z)
• Object Style Diffusion for Generalized Object Detection in Urban Scene [69.04189353993907]
  We introduce a novel single-domain object detection generalization method, named GoDiff.<n>By integrating pseudo-target domain data with source domain data, we diversify the training dataset.<n> Experimental results demonstrate that our method not only enhances the generalization ability of existing detectors but also functions as a plug-and-play enhancement for other single-domain generalization methods.
  arXiv  Detail & Related papers  (2024-12-18T13:03:00Z)
• Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification [71.08024880298613]
  We study the multi-source Domain Generalization of text classification. We propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
  arXiv  Detail & Related papers  (2024-09-20T07:46:21Z)
• Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models [3.072340427031969]
  Few-shot action recognition (FSAR) aims to learn a model capable of identifying novel actions in videos using only a few examples. In assuming the base dataset seen during meta-training and novel dataset used for evaluation can come from different domains, cross-domain few-shot learning alleviates data collection and annotation costs. We systematically evaluate existing state-of-the-art single-domain, transfer-based, and cross-domain FSAR methods on new cross-domain tasks.
  arXiv  Detail & Related papers  (2024-06-03T07:48:18Z)
• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation [48.039156140237615]
  A Continual Test-Time Adaptation task is proposed to adapt the pre-trained model to continually changing target domains. We design a Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific and domain-shared knowledge. Our proposed method achieves state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-06-07T11:18:53Z)
• Robust Representation Learning with Self-Distillation for Domain Generalization [2.0817769887373245]
  We propose a novel domain generalization technique called Robust Representation Learning with Self-Distillation. We observe an average accuracy improvement in the range of 1.2% to 2.3% over the state-of-the-art on three datasets.
  arXiv  Detail & Related papers  (2023-02-14T07:39:37Z)
• DATa: Domain Adaptation-Aided Deep Table Detection Using Visual-Lexical Representations [2.542864854772221]
  We present a novel Domain Adaptation-aided deep Table detection method called DATa. It guarantees satisfactory performance in a specific target domain where few trusted labels are available. Experiments show that DATa substantially outperforms competing methods that only utilize visual representations in the target domain.
  arXiv  Detail & Related papers  (2022-11-12T12:14:16Z)
• Learning to Augment via Implicit Differentiation for Domain Generalization [107.9666735637355]
  Domain generalization (DG) aims to overcome the problem by leveraging multiple source domains to learn a domain-generalizable model. In this paper, we propose a novel augmentation-based DG approach, dubbed AugLearn. AugLearn shows effectiveness on three standard DG benchmarks, PACS, Office-Home and Digits-DG.
  arXiv  Detail & Related papers  (2022-10-25T18:51:51Z)
• Domain Generalization for Activity Recognition via Adaptive Feature Fusion [9.458837222079612]
  We propose emphAdaptive Feature Fusion for Activity Recognition(AFFAR). AFFAR learns to fuse the domain-invariant and domain-specific representations to improve the model's generalization performance. We apply AFAR to a real application, i.e., the diagnosis of Children's Attention Deficit Hyperactivity Disorder(ADHD)
  arXiv  Detail & Related papers  (2022-07-21T02:14:09Z)
• Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
  In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level. Our method produces state-of-the-art results on the C-Driving dataset.
  arXiv  Detail & Related papers  (2021-06-07T08:38:41Z)
• Adversarial Bipartite Graph Learning for Video Domain Adaptation [50.68420708387015]
  Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area. Recent works on visual domain adaptation which leverage adversarial learning to unify the source and target video representations are not highly effective on the videos. This paper proposes an Adversarial Bipartite Graph (ABG) learning framework which directly models the source-target interactions.
  arXiv  Detail & Related papers  (2020-07-31T03:48:41Z)

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.