mlsanityData to 14 Oct 2026

Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning

arXiv 2308.13503 ↗First posted 25 Aug 2023cs.CV cs.AIcited by 3

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Abstract (arXiv)

This work explores various ways of exploring multi-task learning (MTL) techniques aimed at classifying videos as original or manipulated in cross-manipulation scenario to attend generalizability in deep fake scenario. The dataset used in our evaluation is FaceForensics++, which features 1000 original videos manipulated by four different techniques, with a total of 5000 videos. We conduct extensive experiments on multi-task learning and contrastive techniques, which are well studied in literature for their generalization benefits. It can be concluded that the proposed detection model is quite generalized, i.e., accurately detects manipulation methods not encountered during training as compared to the state-of-the-art.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

25 of 48 references.

References · 25

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13 more
  1. 2021Multi-attentional Deepfake Detectioncited by 341
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  3. 2020DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detectioncited by 233
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  5. 2022Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detectioncited by 173
  6. 2022Protecting Celebrities from DeepFake with Identity Consistency Transformercited by 95
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysiscited by 3
  2. 2026Fusion-SSAT: Unleashing the Potential of Self-supervised Auxiliary Task by Feature Fusion for Generalized Deepfake Detectioncited by 0
  3. 2026A Backbone Benchmarking Study on Self-supervised Learning as a Auxiliary Task with Texture-based Local Descriptors for Face Analysiscited by 0