Деформирующие преобразования изображений и их применение при аугментации данных для обучения глубоких нейронных сетей

The paper focuses on the improvement of the quality of learning for deep neural networks for a small data set in a classification task. One of the possible approaches to improve the quality of learning is researched which is based on the use of data augmentation (artificial reproduction of the data set) by image warping. The presented mathematical model and fast algorithm for warping make it possible to transform the original image while preserving its structural basis. The proposed algorithm is used to augment image data sets containing a small number of training samples. The augmentation consists of two stages including horizontal mirroring and warping of each of the samples. The effectiveness of such augmentation is tested through the training of neural networks of various types: convolutional neural networks (CNN) of a standard architecture and deep residual networks (DRN). A specific feature of the implemented approach for the solution of the problem under consideration consists in the refusal to use pre-trained neural networks with a large number of layers as well as further transfer learning, since their application incurs costs in terms of the computational resources. The paper shows that the efficiency of image classification when implementing the proposed method of augmenting training data on small and medium-sized data sets increases to statistically significant values of the metric used.Проведены исследования возможностей аугментации (искусственного размножения) обучающих данных в задаче классификации с использованием деформирующих преобразований обрабатываемых изображений. Представлены математическая модель и быстродействующий алгоритм выполнения деформирующего преобразования изображения, при использовании которых исходное изображение преобразуется с сохранением своей структурной основы и отсутствием краевых эффектов. Предложенный алгоритм используется для аугментации наборов изображений в задаче классификации, содержащих относительно небольшое количество обучающих примеров. Аугментация исходной выборки осуществляется в два этапа, включающих зеркальное отображение и деформирующее преобразование каждого исходного изображения. Для проверки эффективности подобной техники аугментации в статье проводится обучение нейронных сетей – классификаторов различного вида: сверточных сетей стандартной архитектуры (convolutional neural network, CNN) и сетей с остаточными связями (deep residual network, DRN). Особенностью реализуемого подхода при решении рассматриваемой задачи является также отказ от использования предобученных нейронных сетей с большим количеством слоев и дальнейшим переносом обучения, поскольку их применение несет за собой затраты с точки зрения используемого вычислительного ресурса. Показано, что эффективность классификации изображений при реализации предложенного метода аугментации обучающих данных на выборках малого и среднего объема повышается до статистически значимых значений используемой метрики

A Double Siamese Framework for Differential Morphing Attack Detection

Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results

Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in IEEE Transactions on Information Forensics and Security (TIFS

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