MorDeephy: Face Morphing Detection Via Fused Classification
- URL: http://arxiv.org/abs/2208.03110v1
- Date: Fri, 5 Aug 2022 11:39:22 GMT
- Title: MorDeephy: Face Morphing Detection Via Fused Classification
- Authors: Iurii Medvedev, Farhad Shadmand, Nuno Gon\c{c}alves
- Abstract summary: We introduce a novel deep learning strategy for a single image face morphing detection.
It is directed onto learning the deep facial features, which carry information about the authenticity of these features.
Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalising the task of morphing detection to unseen scenarios.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Face morphing attack detection (MAD) is one of the most challenging tasks in
the field of face recognition nowadays. In this work, we introduce a novel deep
learning strategy for a single image face morphing detection, which implies the
discrimination of morphed face images along with a sophisticated face
recognition task in a complex classification scheme. It is directed onto
learning the deep facial features, which carry information about the
authenticity of these features. Our work also introduces several additional
contributions: the public and easy-to-use face morphing detection benchmark and
the results of our wild datasets filtering strategy. Our method, which we call
MorDeephy, achieved the state of the art performance and demonstrated a
prominent ability for generalising the task of morphing detection to unseen
scenarios.
Related papers
- Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach [77.65459419417533]
We propose an automatic dataset expansion technique to support semantics-oriented DeepFake detection tasks.
We also resort to joint embedding of face images and their corresponding labels for prediction.
Our method improves the generalizability of DeepFake detection and renders some degree of model interpretation by providing human-understandable explanations.
arXiv Detail & Related papers (2024-08-29T07:11:50Z) - Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery.
We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph.
We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
arXiv Detail & Related papers (2024-05-14T10:24:19Z) - LAFS: Landmark-based Facial Self-supervised Learning for Face
Recognition [37.4550614524874]
We focus on learning facial representations that can be adapted to train effective face recognition models.
We explore the learning strategy of unlabeled facial images through self-supervised pretraining.
Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks.
arXiv Detail & Related papers (2024-03-13T01:07:55Z) - DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake
Detection [67.3143177137102]
Deepfake detection refers to detecting artificially generated or edited faces in images or videos.
We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
arXiv Detail & Related papers (2023-12-07T07:19:45Z) - Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy.
Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
arXiv Detail & Related papers (2023-10-18T14:49:54Z) - Fused Classification For Differential Face Morphing Detection [0.0]
Face morphing, a presentation attack technique, poses significant security risks to face recognition systems.
Traditional methods struggle to detect morphing attacks, which involve blending multiple face images.
We propose an extended approach based on fused classification method for no-reference scenario.
arXiv Detail & Related papers (2023-09-01T16:14:29Z) - Impact of Image Context for Single Deep Learning Face Morphing Attack
Detection [0.0]
Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks.
This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance.
arXiv Detail & Related papers (2023-09-01T15:57:24Z) - Face Morphing Attack Detection Using Privacy-Aware Training Data [0.991629944808926]
Images of morphed faces pose a serious threat to face recognition--based security systems.
Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals.
This approach raises various privacy concerns and limits the amount of publicly available training data.
arXiv Detail & Related papers (2022-07-02T19:00:48Z) - Detect and Locate: A Face Anti-Manipulation Approach with Semantic and
Noise-level Supervision [67.73180660609844]
We propose a conceptually simple but effective method to efficiently detect forged faces in an image.
The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image.
The proposed model achieves state-of-the-art detection accuracy and remarkable localization performance.
arXiv Detail & Related papers (2021-07-13T02:59:31Z) - 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)
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.