In this work we present Picasso: a lightweight device class fingerprinting protocol that allows a server to verify the software and hardware stack of a mobile or desktop client. As an example, Picasso can distinguish between traffic sent by an authentic iPhone running Safari on iOS from an emulator or desktop client spoofing the same configuration. Our fingerprinting scheme builds on unpredictable yet stable noise introduced by a client's browser, operating system, and graphical stack when rendering HTML5 canvases. Our algorithm is resistant to replay and includes a hardware-bound proof of work that forces a client to expend a configurable amount of CPU and memory to solve challenges. We demonstrate that Picasso can distinguish 52 million Android, iOS, Windows, and OSX clients running a diversity of browsers with 100% accuracy. We discuss applications of Picasso in abuse fighting, including protecting the Play Store or other mobile app marketplaces from inorganic interactions; or identifying login attempts to user accounts from previously unseen device classes.

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The problem of generating symmetry adapted wavefunctions, within the framework of the generator coordinate method, is examined. Results for the discretisation technique and the natural state formalism are applied to the quartic anharmonic oscillator.

The paper presents a two-step approach for detection and identification of fake (forged) fragments in photographic images. At the first step, the image is assigned to an original or a fake class by sequentially analyzing the image metadata (EXIF), then performing the error level analysis (ELA), and finally performing neural network analysis of the image. At the second step, the fake image is divided into fragments, each one is analyzed using a neural network for the fake detection, then the fragments that had recognized as a fake are analyzed by a classifying neural network, which makes it possible to identify specific types of fakes. Software has been developed that implements the proposed approach. The training of the used neural networks was carried out on the CASIA V2 dataset, which contains 4795 images (1701 genuine and 3274 fakes). Studies have been carried out to verify the effectiveness, for which, using the developed software 60 photo images were processed, 30 of which are genuine, and 30 are fakes. The developed software correctly identified both the fake fragments and determined their types.