Robust Deepfake On Unrestricted Media: Generation And Detection
- URL: http://arxiv.org/abs/2202.06228v1
- Date: Sun, 13 Feb 2022 06:53:39 GMT
- Title: Robust Deepfake On Unrestricted Media: Generation And Detection
- Authors: Trung-Nghia Le and Huy H Nguyen and Junichi Yamagishi and Isao Echizen
- Abstract summary: Recent advances in deep learning have led to substantial improvements in deepfake generation.
This chapter explores the evolution of and challenges in deepfake generation and detection.
- Score: 46.576556314444865
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in deep learning have led to substantial improvements in
deepfake generation, resulting in fake media with a more realistic appearance.
Although deepfake media have potential application in a wide range of areas and
are drawing much attention from both the academic and industrial communities,
it also leads to serious social and criminal concerns. This chapter explores
the evolution of and challenges in deepfake generation and detection. It also
discusses possible ways to improve the robustness of deepfake detection for a
wide variety of media (e.g., in-the-wild images and videos). Finally, it
suggests a focus for future fake media research.
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