SAGNet: Decoupling Semantic-Agnostic Artifacts From Limited Training Data for Robust Generalization in Deepfake Detection
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Date
2025
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Journal Article
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Abstract
Deepfake detection presents a significant challenge, particularly when the available training data is constrained to a limited set of semantic categories-a common and realistic scenario. In deepfake detection, the training labels typically indicate whether an image is real or fake, without specifying the semantic content, such as object classes. Moreover, we cannot know in advance the object categories present in an image to be detected. Ideally, a deepfake detection model should perform consistently across different semantic categories during inference, irrespective of the content. However, existing methods often exhibit significant performance bias between seen and unseen classes, struggling to generalize effectively. To address this issue, we propose Semantic-AGnostic artifact Network (SAGNet), an innovative and efficient approach designed to decouple semantic-agnostic artifacts from content-specific distributions in the training data. Our method eliminates semantic-specific biases, ensuring that the model focuses on universal artifacts related to image authenticity rather than content-dependent features. By employing this decoupling strategy, SAGNet greatly enhances the model's generalization capacity, even when trained on limited data. Remarkably, through experiments, we demonstrate that SAGNet achieves performance comparable to models trained with 10 times more data, despite being trained on only 2 classes (comparing SAGNet trained on 2 classes with Ojha et al. (2023) trained on 20 categories). Furthermore, through extensive experiments, we show that SAGNet's improvements are not only evident across different semantic categories but also extend to various generative methods, including multiple GAN-based and diffusion-based models. This cross-method generalization emphasizes SAGNet's versatility and effectiveness in diverse generative scenarios. Overall, our method represents a significant advancement in deepfake detection, particularly in realistic situations where the training data is limited. The code is released at https://github.com/rstao-bjtu/SAGNet/
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published
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20
Pages / Article No.
6429 - 6442
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IEEE
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Subject
Deep learning; computer vision; deepfake detection; robust generalization; AI generation