Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFace identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject's image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects' images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneouslyThis work was supported by the Consejería De Ciencia, Universidad e Innovación, Comunidad de Madri

FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features

Face Spoof Detection Using Convolutional Neural Networks

Department: Engineering and applied sciences Major: 060509 – Computer Science Major: Software of Computer Systems and Networks Supervisor: PhD, Associate Professor Leyla MuradkhanliIn recent years, the use of facial recognition technology has become increasingly prevalent, finding application in various areas, including security, authentication, and access management. With the extensive employment of face recognition technology has come an increase in the prevalence of face spoofing cases, wherein offenders manipulate the system with unauthentic facial information. The emergence of this issue poses a major risk to the dependability and protection of facial recognition technology. This calls for the development of advanced and robust techniques to detect face spoofing effectively. This thesis suggests a technique that employs convolutional neural networks (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fabricated facial images. Two streams are employed in this process. RGB images are transformed to grayscale images in the first stream, and then facial reflection features are extracted. Face color features from RGB images are extracted in the second stream. These two characteristics are then combined and utilized to identify face spoofing. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, I employ a standardized data set for identifying counterfeit or altered facial attributes. The proposed approach has the capability to achieve an average precision rate of 89% while being applied to the provided data set. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire intricate and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility