  In recent years, the spread of fake videos has brought great influence on
individuals and even countries. It is important to provide robust and reliable
results for fake videos. The results of conventional detection methods are not
reliable and not robust for unseen videos. Another alternative and more
effective way is to find the original video of the fake video. For example,
fake videos from the Russia-Ukraine war and the Hong Kong law revision storm
are refuted by finding the original video. We use an improved retrieval method
to find the original video, named ViTHash. Specifically, tracing the source of
fake videos requires finding the unique one, which is difficult when there are
only small differences in the original videos. To solve the above problems, we
designed a novel loss Hash Triplet Loss. In addition, we designed a tool called
Localizator to compare the difference between the original traced video and the
fake video. We have done extensive experiments on FaceForensics++, Celeb-DF and
DeepFakeDetection, and we also have done additional experiments on our built
three datasets: DAVIS2016-TL (video inpainting), VSTL (video splicing) and DFTL
(similar videos). Experiments have shown that our performance is better than
state-of-the-art methods, especially in cross-dataset mode. Experiments also
demonstrated that ViTHash is effective in various forgery detection: video
inpainting, video splicing and deepfakes. Our code and datasets have been
released on GitHub: \url{https://github.com/lajlksdf/vtl}.
