We lifted 5 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| AlgoHunt/Face-Xray | reimplementation | 2 of 2 |
| neverUseThisName/Face-X-Ray | reimplementation | 1 of 1 |
| wkq-wukaiqi/Face-X-Ray | reimplementation | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| forge | Ran | neverUseThisName/Face-X-Ray/faceBlending.py pointer only (licence: NONE) · get_code("5a25a28d8af84798") |
| name_resolve | Ran | AlgoHunt/Face-Xray/bi_online_generation.py pointer only (licence: GPL-3.0) · get_code("058d9031749d6578") |
| total_euclidean_distance | Ran | AlgoHunt/Face-Xray/bi_online_generation.py pointer only (licence: GPL-3.0) · get_code("46bd3644050f092a") |
| get_boundingbox | Not yet run | wkq-wukaiqi/Face-X-Ray/detect_video.py pointer only (licence: NONE) · get_code("1da4deb7636abba1") |
| test | Not yet run | wkq-wukaiqi/Face-X-Ray/evaluate.py pointer only (licence: NONE) · get_code("c24c6d6400fe185b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We observe that most existing face manipulation methods share a common step: blending the altered face into an existing background image. For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms. Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1912.13458")
get_code_for_paper("1912.13458")
have("1912.13458")
Connect an agent — have() is free.