| dc.description.abstract | This paper proposes a deepfake detection model that combines blended and optical flow
features, offering effective improvements in the ability to identify various forgery methods.
In the context of rapid advancements in deep learning, its applications extend beyond image
recognition to domains such as recommendation systems and medical diagnostics, profoundly
impacting daily life. However, alongside these advancements come potential risks, such as the
misuse of technologies that threaten privacy and security, exemplified by deepfake technology.
Deepfake technology has become an increasingly pressing issue due to its ability to gen-
erate fake images and synthesize realistic videos. As the technology evolves, forgery methods
continue to emerge, posing challenges to training deepfake detection models. These challenges
include the need for large datasets to support model training, while acquiring data for novel
forgery methods remains a significant obstacle.
The rapid development of deepfake technology has raised critical concerns about the cross-
manipulation problem. Cross-manipulation refers to the requirement for a model trained to
recognize specific types of forgeries to generalize effectively against unseen forgery methods.
This necessitates stronger generalization capabilities, further complicating the task of identifying
authenticity. Additionally, this issue extends to the cross-dataset problem, where the model must
not only identify various forgery techniques but also address distributional differences between
datasets that lead to a decline in detection accuracy.
To address the cross-dataset problem, this study proposes a forgery detection method that
integrates dynamic and static features. The model demonstrates significant improvements in
detection performance during cross-dataset testing and maintains stable and efficient detection
capabilities in cross-manipulation scenarios.
Experimental results highlight the advantages of combining dynamic and static features.
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Dynamic features capture subtle variations between consecutive frames, particularly temporal
anomalies introduced by forgery techniques. Meanwhile, static features effectively extract local
details and texture information from individual frames. By integrating these two types of fea-
tures, the model achieves more accurate forgery detection, demonstrating robust classification
performance and superior generalization in cross-manipulation scenarios.
In summary, this study provides an innovative and robust solution to deepfake detection,
addressing both cross-dataset and cross-manipulation challenges, and laying a solid foundation
for future research in this critical area. | en_US |