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Unveiling the Impact of Image Transformations on Deepfake Detection: An Experimental Analysis

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Image Analysis and Processing – ICIAP 2023 (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14234))

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Abstract

With the recent explosion of interest in visual Generative AI, the field of deepfake detection has gained a lot of attention. In fact, deepfake detection might be the only measure to counter the potential proliferation of generated media in support of fake news and its consequences. While many of the available works limit the detection to a pure and direct classification of fake versus real, this does not translate well to a real-world scenario. Indeed, malevolent users can easily apply post-processing techniques to generated content, changing the underlying distribution of fake data. In this work, we provide an in-depth analysis of the robustness of a deepfake detection pipeline, considering different image augmentations, transformations, and other pre-processing steps. These transformations are only applied in the evaluation phase, thus simulating a practical situation in which the detector is not trained on all the possible augmentations that can be used by the attacker. In particular, we analyze the performance of a \(k\)-NN and a linear probe detector on the COCOFake dataset, using image features extracted from pre-trained models, like CLIP and DINO. Our results demonstrate that while the CLIP visual backbone outperforms DINO in deepfake detection with no augmentation, its performance varies significantly in presence of any transformation, favoring the robustness of DINO.

F. Cocchi and L. Baraldi—Equal contribution.

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Acknowledgments

This work has partially been supported by the European Commission under the PNRR-M4C2 (PE00000013) project “FAIR - Future Artificial Intelligence Research” and by the Horizon Europe project “European Lighthouse on Safe and Secure AI (ELSA)” (HORIZON-CL4-2021-HUMAN-01-03), co-funded by the European Union (GA 101070617).

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Authors and Affiliations

  1. University of Modena and Reggio Emilia, Modena, Italy

    Federico Cocchi, Samuele Poppi, Marcella Cornia, Lorenzo Baraldi & Rita Cucchiara

  2. University of Pisa, Pisa, Italy

    Federico Cocchi, Lorenzo Baraldi & Samuele Poppi

  3. IIT-CNR, Pisa, Italy

    Rita Cucchiara

Authors
  1. Federico Cocchi
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  2. Lorenzo Baraldi
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  3. Samuele Poppi
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  4. Marcella Cornia
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  5. Lorenzo Baraldi
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  6. Rita Cucchiara
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Corresponding author

Correspondence to Lorenzo Baraldi .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Cocchi, F., Baraldi, L., Poppi, S., Cornia, M., Baraldi, L., Cucchiara, R. (2023). Unveiling the Impact of Image Transformations on Deepfake Detection: An Experimental Analysis. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_29

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  • DOI: https://doi.org/10.1007/978-3-031-43153-1_29

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