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FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network

Abstract : Creating fake images and videos such as “Deepfake” has become much easier these days due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent research such as the few-shot learning can create highly realistic personalized fake images with only a few images. Therefore, the threat of Deepfake to be used for a variety of malicious intents such as propagating fake images and videos becomes prevalent. And detecting these machine-generated fake images has been more challenging than ever.In this work, we propose a light-weight robust fine-tuning neural network-based classifier architecture called Fake Detection Fine-tuning Network (FDFtNet), which is capable of detecting many of the new fake face image generation models, and can be easily combined with existing image classification networks and fine-tuned on a few datasets. In contrast to many existing methods, our approach aims to reuse popular pre-trained models with only a few images for fine-tuning to effectively detect fake images. The core of our approach is to introduce an image-based self-attention module called Fine-Tune Transformer that uses only the attention module and the down-sampling layer. This module is added to the pre-trained model and fine-tuned on a few data to search for new sets of feature space to detect fake images. We experiment with our FDFtNet on the GANs-based dataset (Progressive Growing GAN) and Deepfake-based dataset (Deepfake and Face2Face) with a small input image resolution of 64$$\times $$×64 that complicates detection. Our FDFtNet achieves an overall accuracy of 90.29% in detecting fake images generated from the GANs-based dataset, outperforming the state-of-the-art.
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Submitted on : Monday, November 22, 2021 - 3:33:59 PM
Last modification on : Monday, November 22, 2021 - 4:37:37 PM
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Hyeonseong Jeon, Youngoh Bang, Simon S. Woo. FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network. 35th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Sep 2020, Maribor, Slovenia. pp.416-430, ⟨10.1007/978-3-030-58201-2_28⟩. ⟨hal-03440846⟩



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