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

Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images

Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm. With this motivation, as the first contribution, the article presents a unified PAD algorithm for different kinds of attacks such as printed photos, a replay of video, 3D masks, silicone masks, and wax faces. The proposed algorithm utilizes a combination of wavelet decomposed raw input images from sensor and face region data to detect whether the input image is bonafide or attacked. The second contribution of the article is the collection of a large presentation attack database in the NIR spectrum, containing images from individuals of two ethnicities. The database contains 500 print attack videos which comprise approximately 1,00,000 frames collectively in the NIR spectrum. Extensive evaluation of the algorithm on NIR images as well as visible spectrum images obtained from existing benchmark databases shows that the proposed algorithm yields state-of-the-art results and surpassed several complex and state-of-the-art algorithms. For instance, on benchmark datasets, namely CASIA-FASD, Replay-Attack, and MSU-MFSD, the proposed algorithm achieves a maximum error of 0.92% which is significantly lower than state-of-the-art attack detection algorithms

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field