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Fake visual content detection using two-stream convolutional neural networks

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Abstract

Rapid progress in adversarial learning has enabled the generation of realistic-looking fake visual content. To distinguish between fake and real visual content, several detection techniques have been proposed. The performance of most of these techniques however drops off significantly if the test and the training data are sampled from different distributions. This motivates efforts towards improving the generalization of fake detectors. Since current fake content generation techniques do not accurately model the frequency spectrum of the natural images, we observe that the frequency spectrum of the fake visual data contains discriminative characteristics that can be used to detect fake content. We also observe that the information captured in the frequency spectrum is different from that of the spatial domain. Using these insights, we propose to complement frequency and spatial domain features using a two-stream convolutional neural network architecture called TwoStreamNet. We demonstrate the improved generalization of the proposed two-stream network to several unseen generation architectures, datasets, and techniques. The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors with the fusing of frequency and spatial domain streams also improving the generalization of the detector.

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Notes

  1. The eyes and mouth are determined as the mesoscopic features in the forgery detection in the Deepfake videos.

  2. https://www.yf.io/p/lsun.

  3. https://peterwang512.github.io/CNNDetection/.

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Authors and Affiliations

  1. Department of Computer Science, Information Technology University (ITU), Lahore, Pakistan

    Bilal Yousaf, Waqas Sultani & Arif Mahmood

  2. Lahore University of Management Sciences (LUMS), Lahore, Pakistan

    Muhammad Usama

  3. Department of Computer Science and Engineering (CSE), College of Engineering, Qatar University, Doha, Qatar

    Junaid Qadir

  4. Department of Electrical Engineering, Information Technology University (ITU), Lahore, Pakistan

    Junaid Qadir

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  1. Bilal Yousaf
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  2. Muhammad Usama
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  3. Waqas Sultani
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Correspondence to Junaid Qadir.

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Yousaf, B., Usama, M., Sultani, W. et al. Fake visual content detection using two-stream convolutional neural networks. Neural Comput & Applic 34, 7991–8004 (2022). https://doi.org/10.1007/s00521-022-06902-5

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