Deepfake attribution: On the source identification of artificially generated images

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5 Citations (Scopus)


Synthetic media or "deepfakes" are making great advances in visual quality, diversity, and verisimilitude, empowered by large-scale publicly accessible datasets and rapid technical progress in deep generative modeling. Heralding a paradigm shift in how online content is trusted, researchers in digital image forensics have responded with different proposals to reliably detect AI-generated images in the wild. However, binary classification of image authenticity is insufficient to regulate the ethical usage of deepfake technology as new applications are developed. This article provides an overview of the major innovations in synthetic forgery detection as of 2020, while highlighting the recent shift in research towards ways to attribute AI-generated images to their generative sources with evidence. We define the various categories of deepfakes in existence, the subtle processing traces and fingerprints that distinguish AI-generated images from reality and each other, and the different degrees of attribution possible with current understanding of generative algorithms. Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter-forensic attacks devised to exploit these limitations, and directions for new research to assure the long-term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI-enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology.

Original languageEnglish
Article numbere1438
Number of pages21
JournalWIREs Data Mining and Knowledge Discovery
Issue number3
Publication statusPublished - May 2022


  • computer vision
  • deepfakes
  • machine learning
  • source identification
  • synthetic media

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