Facial Manipulation Detection Based on the Color Distribution Analysis
in Edge Region
- URL: http://arxiv.org/abs/2102.01381v1
- Date: Tue, 2 Feb 2021 08:19:35 GMT
- Title: Facial Manipulation Detection Based on the Color Distribution Analysis
in Edge Region
- Authors: Dong-Keon Kim, DongHee Kim, and Kwangsu Kim
- Abstract summary: We present a generalized and robust facial manipulation detection method based on color distribution analysis of the vertical region of edge in a manipulated image.
Our extensive experiments show that our method outperforms other existing face manipulation detection methods on detecting synthesized face image in various datasets regardless of whether it has participated in training.
- Score: 0.5735035463793008
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we present a generalized and robust facial manipulation
detection method based on color distribution analysis of the vertical region of
edge in a manipulated image. Most of the contemporary facial manipulation
method involves pixel correction procedures for reducing awkwardness of pixel
value differences along the facial boundary in a synthesized image. For this
procedure, there are distinctive differences in the facial boundary between
face manipulated image and unforged natural image. Also, in the forged image,
there should be distinctive and unnatural features in the gap distribution
between facial boundary and background edge region because it tends to damage
the natural effect of lighting. We design the neural network for detecting
face-manipulated image with these distinctive features in facial boundary and
background edge. Our extensive experiments show that our method outperforms
other existing face manipulation detection methods on detecting synthesized
face image in various datasets regardless of whether it has participated in
training.
Related papers
- Human Face Recognition from Part of a Facial Image based on Image
Stitching [0.0]
Most of the current techniques for face recognition require the presence of a full face of the person to be recognized.
In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture.
The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods.
arXiv Detail & Related papers (2022-03-10T19:31:57Z) - Robust Face-Swap Detection Based on 3D Facial Shape Information [59.32489266682952]
Face-swap images and videos have attracted more and more malicious attackers to discredit some key figures.
Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues.
We propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures.
arXiv Detail & Related papers (2021-04-28T09:35:48Z) - Face Forgery Detection by 3D Decomposition [72.22610063489248]
We consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment.
By disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture.
We propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns.
arXiv Detail & Related papers (2020-11-19T09:25:44Z) - DeepFake Detection Based on the Discrepancy Between the Face and its
Context [94.47879216590813]
We propose a method for detecting face swapping and other identity manipulations in single images.
Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context.
We describe a method which uses the recognition signals from our two networks to detect such discrepancies.
Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
arXiv Detail & Related papers (2020-08-27T17:04:46Z) - InterFaceGAN: Interpreting the Disentangled Face Representation Learned
by GANs [73.27299786083424]
We propose a framework called InterFaceGAN to interpret the disentangled face representation learned by state-of-the-art GAN models.
We first find that GANs learn various semantics in some linear subspaces of the latent space.
We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection.
arXiv Detail & Related papers (2020-05-18T18:01:22Z) - Face Anti-Spoofing by Learning Polarization Cues in a Real-World
Scenario [50.36920272392624]
Face anti-spoofing is the key to preventing security breaches in biometric recognition applications.
Deep learning method using RGB and infrared images demands a large amount of training data for new attacks.
We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
arXiv Detail & Related papers (2020-03-18T03:04:03Z) - Robust Facial Landmark Detection via Aggregation on Geometrically
Manipulated Faces [32.391300491317445]
We equip our method with the aggregation of manipulated face images.
Small but carefully crafted geometric manipulation in the input domain can fool deep face recognition models.
Our approach is demonstrated its superiority compared to the state-of-the-art method on benchmark datasets AFLW, 300-W, and COFW.
arXiv Detail & Related papers (2020-01-07T16:43:09Z) - Face X-ray for More General Face Forgery Detection [45.59018645493997]
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.
arXiv Detail & Related papers (2019-12-31T17:57:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.