May 25, 2022 · Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.
These attacks build on the concept of a GAN fingerprint, a consistent frequency pattern that characterizes the generation process similar to a camera.
Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.
This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning‐based approaches.
May 25, 2022 · Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.
We show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image.
... We analyzed several novel studies concerning deepfake detection using the most recent deep learning-inspired architectures, providing a more detailed On the ...
Misleading Deep-Fake Detection with GAN Fingerprints. Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring. January, 2022. Cite DOI. Type. Conference paper.
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Misleading deep-fake detection with GAN fingerprints. Vera Wesselkamp,; Konrad Rieck · ORCID. RUB Icon. ,; Daniel Arp,; Erwin Quiring. RUB Icon.
Jul 19, 2024 · Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and ...