scholarly journals Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Computers ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 117
Author(s):  
Clemens Seibold ◽  
Anna Hilsmann ◽  
Peter Eisert
Keyword(s):  
Deep Neural Network ◽  
Face Image ◽  
Image Manipulation ◽  
Automated Verification ◽  
Decision Making Process ◽  
Border Control ◽  
Face Images ◽  
Verification Systems ◽  
Face Morphing ◽  
Facial Images

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.

Improving Detection of Manipulated Passport Photos - Training Course for Border Control Inspectors to Detect Morphed Facial Passport Photos - Part I: Introduction, State-of-the-Art and Preparatory Tests and Experiments

2021 ◽  
Vol 2021 (3) ◽  
pp. 136-1-136-9
Author(s):  
Franziska Schwarz ◽  
Klaus Schwarz ◽  
Reiner Creutzburg
Keyword(s):  
Border Control ◽  
Facial Image ◽  
Application Process ◽  
Training Course ◽  
Identity Document ◽  
Identity Card ◽  
Identity Cards ◽  
Pass Through ◽  
Face Morphing ◽  
Facial Images

In recent years, ID controllers have observed an increase in the use of fraudulently obtained ID documents [1]. This often involves deception during the application process to get a genuine document with a manipulated passport photo. One of the methods used by fraudsters is the presentation of a morphed facial image. Face morphing is used to assign multiple identities to a biometric passport photo. It is possible to modify the photo so that two or more persons, usually the known applicant and one or more unknown companions, can use the passport to pass through a border control [2]. In this way, persons prohibited from crossing a border can cross it unnoticed using a face morphing attack and thus acquire a different identity. The face morphing attack aims to weaken the application for an identity card and issue a genuine identity document with a morphed facial image. A survey among experts at the Security Printers Conference revealed that a relevant number of at least 1,000 passports with morphed facial images had been detected in the last five years in Germany alone [1]. Furthermore, there are indications of a high number of unreported cases. This high presumed number of unreported cases can also be explained by the lack of morphed photographs’ detection capabilities. Such identity cards would be recognized if the controllers could recognize the morphed facial images. Various studies have shown that the human eye has a minimal ability to recognize morphed faces as such [2], [3], [4], [5], [6]. This work consists of two parts. Both parts are based on the complete development of a training course for passport control officers to detect morphed facial images. Part one contains the conception and the first test trials of how the training course has to be structured to achieve the desired goals and thus improve the detection of morphed facial images for passport inspectors. The second part of this thesis will include the complete training course and the evaluation of its effectiveness.

Lighting-Effects Classification in Facial Images Using Wavelet Packets Transform

2003 ◽  
Vol 01 (02) ◽  
pp. 199-215 ◽  
Author(s):  
A. Z. Kouzani ◽  
S. H. Ong
Keyword(s):  
Recognition System ◽  
Face Image ◽  
Wavelet Packets ◽  
Crucial Step ◽  
Lighting Conditions ◽  
Face Images ◽  
Face Recognition System ◽  
Test Sets ◽  
Multivariate Discriminant ◽  
Facial Images

Faces often produce inconsistent features under different lighting conditions. Classifying lighting effects within a face image is therefore the first crucial step of building a lighting invariant face recognition system. This paper presents a hybrid system to classify face images based on the lighting effects present in the image. The theories of multivariate discriminant analysis and wavelet packets transform are utilised to develop the proposed system. An extensive set of face images of different poses, illuminated from different angles, are used to train the system. The performance of the proposed system is evaluated by conducting experiments on different test sets and by comparing its results against those of some existing counterparts.

scholarly journals Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

2020 ◽  
Author(s):  
Sushma Venkatesh ◽  
Raghavendra Ramachandra ◽  
kiran Raja ◽  
Luuk J. Spreeuwers ◽  
Raymond Veldhuis ◽  
...  
Keyword(s):  
State Of The Art ◽  
Face Image ◽  
Attack Detection ◽  
Security Risk ◽  
Multi Scale ◽  
Residual Noise ◽  
Face Images ◽  
The Face ◽  
Recognition Systems ◽  
Face Morphing

<p> Along with the deployment of the Face Recognition Systems</p> <p>(FRS), concerns were raised related to the vulnerability</p> <p>of those systems towards various attacks including morphed</p> <p>attacks. The morphed face attack involves two different</p> <p>face images in order to obtain via a morphing process</p> <p>a resulting attack image, which is sufficiently similar</p> <p>to both contributing data subjects. The obtained morphed</p> <p>image can successfully be verified against both subjects visually</p> <p>(by a human expert) and by a commercial FRS. The</p> <p>face morphing attack poses a severe security risk to the</p> <p>e-passport issuance process and to applications like border</p> <p>control, unless such attacks are detected and mitigated.</p> <p>In this work, we propose a new method to reliably detect</p> <p>a morphed face attack using a newly designed denoising</p> <p>framework. To this end, we design and introduce a new</p> <p>deep Multi-scale Context Aggregation Network (MS-CAN)</p> <p>to obtain denoised images, which is subsequently used to</p> <p>determine if an image is morphed or not. Extensive experiments</p> <p>are carried out on three different morphed face image</p> <p>datasets. The Morphing Attack Detection (MAD) performance</p> <p>of the proposed method is also benchmarked against</p> <p>14 different state-of-the-art techniques using the ISO-IEC</p> <p>30107-3 evaluation metrics. Based on the obtained quantitative</p> <p>results, the proposed method has indicated the best</p> <p>performance on all three datasets and also on cross-dataset</p> <p>experiments.</p>

scholarly journals Detecting Morphing Attacks through Face Geometry Features

2020 ◽  
Vol 6 (11) ◽  
pp. 115
Author(s):  
Stephanie Autherith ◽  
Cecilia Pasquini
Keyword(s):  
Face Verification ◽  
Border Control ◽  
Control Applications ◽  
Facial Landmarks ◽  
Security Issues ◽  
Supervised Classifiers ◽  
The Subject ◽  
Automated Algorithms ◽  
Verification Systems ◽  
Face Morphing

Face-morphing operations allow for the generation of digital faces that simultaneously carry the characteristics of two different subjects. It has been demonstrated that morphed faces strongly challenge face-verification systems, as they typically match two different identities. This poses serious security issues in machine-assisted border control applications and calls for techniques to automatically detect whether morphing operations have been previously applied on passport photos. While many proposed approaches analyze the suspect passport photo only, our work operates in a differential scenario, i.e., when the passport photo is analyzed in conjunction with the probe image of the subject acquired at border control to verify that they correspond to the same identity. To this purpose, in this study, we analyze the locations of biologically meaningful facial landmarks identified in the two images, with the goal of capturing inconsistencies in the facial geometry introduced by the morphing process. We report the results of extensive experiments performed on images of various sources and under different experimental settings showing that landmark locations detected through automated algorithms contain discriminative information for identifying pairs with morphed passport photos. Sensitivity of supervised classifiers to different compositions on the training and testing sets are also explored, together with the performance of different derived feature transformations.

scholarly journals Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

2020 ◽  
Author(s):  
Sushma Venkatesh ◽  
Raghavendra Ramachandra ◽  
kiran Raja ◽  
Luuk J. Spreeuwers ◽  
Raymond Veldhuis ◽  
...  
Keyword(s):  
State Of The Art ◽  
Face Image ◽  
Attack Detection ◽  
Security Risk ◽  
Multi Scale ◽  
Residual Noise ◽  
Face Images ◽  
The Face ◽  
Recognition Systems ◽  
Face Morphing

<p> Along with the deployment of the Face Recognition Systems</p> <p>(FRS), concerns were raised related to the vulnerability</p> <p>of those systems towards various attacks including morphed</p> <p>attacks. The morphed face attack involves two different</p> <p>face images in order to obtain via a morphing process</p> <p>a resulting attack image, which is sufficiently similar</p> <p>to both contributing data subjects. The obtained morphed</p> <p>image can successfully be verified against both subjects visually</p> <p>(by a human expert) and by a commercial FRS. The</p> <p>face morphing attack poses a severe security risk to the</p> <p>e-passport issuance process and to applications like border</p> <p>control, unless such attacks are detected and mitigated.</p> <p>In this work, we propose a new method to reliably detect</p> <p>a morphed face attack using a newly designed denoising</p> <p>framework. To this end, we design and introduce a new</p> <p>deep Multi-scale Context Aggregation Network (MS-CAN)</p> <p>to obtain denoised images, which is subsequently used to</p> <p>determine if an image is morphed or not. Extensive experiments</p> <p>are carried out on three different morphed face image</p> <p>datasets. The Morphing Attack Detection (MAD) performance</p> <p>of the proposed method is also benchmarked against</p> <p>14 different state-of-the-art techniques using the ISO-IEC</p> <p>30107-3 evaluation metrics. Based on the obtained quantitative</p> <p>results, the proposed method has indicated the best</p> <p>performance on all three datasets and also on cross-dataset</p> <p>experiments.</p>

scholarly journals Enhanced use of gaze cue in a face-following task after brief trial experience in individuals with autism spectrum disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Takao Fukui ◽  
Mrinmoy Chakrabarty ◽  
Misako Sano ◽  
Ari Tanaka ◽  
Mayuko Suzuki ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Face Image ◽  
Fixation Time ◽  
Gaze Shift ◽  
The Gaze ◽  
Face Images ◽  
Trial Experience ◽  
Short Period ◽  
The Face ◽  
Gaze Cues

AbstractEye movements toward sequentially presented face images with or without gaze cues were recorded to investigate whether those with ASD, in comparison to their typically developing (TD) peers, could prospectively perform the task according to gaze cues. Line-drawn face images were sequentially presented for one second each on a laptop PC display, and the face images shifted from side-to-side and up-and-down. In the gaze cue condition, the gaze of the face image was directed to the position where the next face would be presented. Although the participants with ASD looked less at the eye area of the face image than their TD peers, they could perform comparable smooth gaze shift to the gaze cue of the face image in the gaze cue condition. This appropriate gaze shift in the ASD group was more evident in the second half of trials in than in the first half, as revealed by the mean proportion of fixation time in the eye area to valid gaze data in the early phase (during face image presentation) and the time to first fixation on the eye area. These results suggest that individuals with ASD may benefit from the short-period trial experiment by enhancing the usage of gaze cue.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

scholarly journals RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

A Novel Illumination Compensation Method Using Wavelet Packet Transformation

2011 ◽  
Vol 317-319 ◽  
pp. 897-900
Author(s):  
Zhen Hua Zhao ◽  
Xiao Hong Hao
Keyword(s):  
Multiplicative Noise ◽  
Additive Noise ◽  
Recognition Performance ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Results ◽  
Illumination Compensation ◽  
Wavelet Packet Transformation ◽  
Facial Images

A novel illumination compensate method is proposed in this paper to improve recognition performance. A modified lighting model called Lambertin which includes additive noise and multiplicative noise are presented firstly. Then, additive noise is removed by using wavelet packet transformation. Next, the processed image is transformed into logarithm domain and the multiplicative noise, which has been named additive noise, is removed by means of the same above algorithm. Finally, a compensated face image is obtained. We examine the proposed method on Yale extended B database compared with other methods. Experimental results show that our algorithm improves by 3%~12% recognition rate. It can effectively adjust the facial images for varying illumination conditions and also improve the recognition performance and robustness.

Prioritizing Attention over Privacy: Analysis of Face Image Posting Behavior in Online Direct-to-Consumer Genetic Testing Forum (Preprint)

2021 ◽  
Author(s):  
Yongtai Liu ◽  
Zhijun Yin ◽  
Zhiyu Wan ◽  
Chao Yan ◽  
Weiyi Xia ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Language Processing ◽  
Face Image ◽  
Online Forums ◽  
The Public ◽  
Image Sharing ◽  
Direct To Consumer ◽  
Face Images ◽  
Identity Disclosure ◽  
The Face

BACKGROUND As direct-to-consumer genetic testing (DTC-GT) services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so under a pseudonym, but more recently, they have included face images when discussing DTC-GT results. When these images truthfully represent a user, they reveal the identity of the corresponding individual. Various studies have shown that sharing images in social media tends to elicit more replies. However, users who do this clearly forgo their privacy. OBJECTIVE This study aimed to investigate the face image sharing behavior of DTC-GT users in an online environment and determine if there exists the association between face image sharing and the attention received from others. METHODS This study focused on r/23andme, a subreddit dedicated to discussing DTC-GT results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to learn the association between the attention that a post received, in terms of the number of comments and karma scores (defined as the number of upvotes minus the number of downvotes), and whether the post contains a face image. RESULTS We collected over 15,000 posts from the r/23andme subreddit published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals’ revealing their faces by early 2020. The topics in posts including a face were primarily about sharing or discussing ancestry composition, and sharing family reunion photos with relatives discovered via DTC-GT. On average, posts including a face received 60% (5/8) more comments than other posts, and these posts had karma scores 2.4 times higher than other posts. CONCLUSIONS DTC-GT consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate the risk of face image posting, platforms, or at least subreddit organizers, should inform users about the consequence of such behavior for identity disclosure.

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