Application of Benford's law in deepfake image detection

Разработка и совершенствование технологий обнаружения deepfake являются одним из приоритетных направлений обеспечения социальной и биометрической безопасности. В работе исследуются перспективы применения закона Бенфорда как инструмента обнаружения deepfake-изображений, сгенерированных нейросетями GAN. Предлагаемый подход основан на анализе спектра мощности и энтропии изобра-жений. Эффективность предложенного метода апробировалась на датасетах, сгенерированных нейросетями StyleGAN2 и StyleGAN3. Предложенный метод не требует больших вычислительных мощностей

Auditing Symposium XIII: Proceedings of the 1996 Deloitte & Touche/University of Kansas Symposium on Auditing Problems

Meeting the challenge of technological change -- A standard setter\u27s perspective / James M. Sylph, Gregory P. Shields; Technological change -- A glass half empty or a glass half full: Discussion of Meeting the challenge of technological change, and Business and auditing impacts of new technologies / Urton Anderson; Opportunities for assurance services in the 21st century: A progress report of the Special Committee on Assurance Services / Richard Lea; Model of errors and irregularities as a general framework for risk-based audit planning / Jere R. Francis, Richard A. Grimlund; Discussion of A Model of errors and irregularities as a general framework for risk-based audit planning / Timothy B. Bell; Framing effects and output interference in a concurring partner review context: Theory and exploratory analysis / Karla M. Johnstone, Stanley F. Biggs, Jean C. Bedard; Discussant\u27s comments on Framing effects and output interference in a concurring partner review context: Theory and exploratory analysis / David Plumlee; Implementation and acceptance of expert systems by auditors / Maureen McGowan; Discussion of Opportunities for assurance services in the 21st century: A progress report of the Special Committee on Assurance Services / Katherine Schipper; CPAS/CCM experiences: Perspectives for AI/ES research in accounting / Miklos A. Vasarhelyi; Discussant comments on The CPAS/CCM experiences: Perspectives for AI/ES research in accounting / Eric Denna; Digital analysis and the reduction of auditor litigation risk / Mark Nigrini; Discussion of Digital analysis and the reduction of auditor litigation risk / James E. Searing; Institute of Internal Auditors: Business and auditing impacts of new technologies / Charles H. Le Grandhttps://egrove.olemiss.edu/dl_proceedings/1012/thumbnail.jp

Face morphing detection in the presence of printing/scanning and heterogeneous image sources

Face morphing represents nowadays a big security threat in the context of electronic identity documents as well as an interesting challenge for researchers in the field of face recognition. Despite of the good performance obtained by state-of-the-art approaches on digital images, no satisfactory solutions have been identified so far to deal with cross-database testing and printed-scanned images (typically used in many countries for document issuing). In this work, novel approaches are proposed to train Deep Neural Networks for morphing detection: in particular generation of simulated printed-scanned images together with other data augmentation strategies and pre-training on large face recognition datasets, allowed to reach state-of-the-art accuracy on challenging datasets from heterogeneous image sources

Palmprint Gender Classification Using Deep Learning Methods

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification

The regulation of digital platforms: the case of pagoPA

How can EU regulation affect innovation. Digital revolution: How big data have changed the world and the legal landscape. The regulation of digital platforms in Europe. Digital revolution: How distributed ledger technologies are changing the world and the legal landscape. Regulation of digital payments: the case of pagopa