Analyzing eyebrow region for morphed image detection
- URL: http://arxiv.org/abs/2310.19290v1
- Date: Mon, 30 Oct 2023 06:11:27 GMT
- Title: Analyzing eyebrow region for morphed image detection
- Authors: Abdullah Zafar, Christoph Busch,
- Abstract summary: The proposed method is based on analyzing the frequency content of the eyebrow region.
The findings suggest that the proposed method can serve as a valuable tool in morphed image detection.
- Score: 4.879461135691896
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Facial images in passports are designated as primary identifiers for the verification of travelers according to the International Civil Aviation Organization (ICAO). Hence, it is important to ascertain the sanctity of the facial images stored in the electronic Machine-Readable Travel Document (eMRTD). With the introduction of automated border control (ABC) systems that rely on face recognition for the verification of travelers, it is even more crucial to have a system to ensure that the image stored in the eMRTD is free from any alteration that can hinder or abuse the normal working of a facial recognition system. One such attack against these systems is the face-morphing attack. Even though many techniques exist to detect morphed images, morphing algorithms are also improving to evade these detections. In this work, we analyze the eyebrow region for morphed image detection. The proposed method is based on analyzing the frequency content of the eyebrow region. The method was evaluated on two datasets that each consisted of morphed images created using two algorithms. The findings suggest that the proposed method can serve as a valuable tool in morphed image detection, and can be used in various applications where image authenticity is critical.
Related papers
- ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification [60.73617868629575]
misuse of deep learning-based facial manipulation poses a potential threat to civil rights.
To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process.
We propose a novel universal framework for combating facial manipulation, called ID-Guard.
arXiv Detail & Related papers (2024-09-20T09:30:08Z) - TetraLoss: Improving the Robustness of Face Recognition against Morphing
Attacks [7.092869001331781]
Face recognition systems are widely deployed in high-security applications.
Digital manipulations, such as face morphing, pose a security threat to face recognition systems.
We present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks.
arXiv Detail & Related papers (2024-01-21T21:04:05Z) - 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) - 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) - Psychophysical Evaluation of Human Performance in Detecting Digital Face
Image Manipulations [14.63266615325105]
This work introduces a web-based, remote visual discrimination experiment on the basis of principles adopted from the field of psychophysics.
We examine human proficiency in detecting different types of digitally manipulated face images, specifically face swapping, morphing, and retouching.
arXiv Detail & Related papers (2022-01-28T12:45:33Z) - Single Morphing Attack Detection using Feature Selection and
Visualisation based on Mutual Information [13.725021925072603]
This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter.
The eyes and nose are identified as the most critical areas to be analysed.
arXiv Detail & Related papers (2021-10-26T10:27:06Z) - 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) - Harnessing Unrecognizable Faces for Face Recognition [87.80037162457427]
We propose a measure of recognizability of a face image, implemented by a deep neural network trained using mostly recognizable identities.
We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5.
arXiv Detail & Related papers (2021-06-08T05:25:03Z) - 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 Morphing Attack Generation & Detection: A Comprehensive Survey [12.936155415524937]
Face Recognition System (FRS) has received a great interest from the biometric community.
The goal of a morphing attack is to subvert the FRS at Automatic Border Control gates.
Malicious actor and accomplice can generate morphed face image and obtain e-passport.
arXiv Detail & Related papers (2020-11-03T22:36:27Z) - 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)
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.