An Universal Adversarial Attack Method Based on Spherical Projection

Adversarial attack on neural networks has become an important problem restricting its security applications, and among adversarial attacks oriented towards the sample set, the universal perturbation design causing most sample output errors is critical to the study. This paper takes the neural network for image classification as the research object, summarizes the existing universal perturbation generation algorithm, proposes a universal perturbation generation algorithm combining batch stochastic gradient rise and spherical projection search, achieves loss function reduction through the iterative training of stochastic gradient rise in batch samples, and limits the universal perturbation search to a high-dimensional sphere with radius [Formula: see text] to reduce the search space of universal perturbation. Moreover, the regularized technology is introduced to improve the generation quality of universal perturbations. The experimental results show that compared with the baseline algorithm, the attack success rate increases by more than 10%, the solution efficiency of universal perturbation is improved by one order of magnitude, and the quality controllability of universal perturbation is better.

360-Degree VR Video Watermarking Based on Spherical Wavelet Transform

Similar to conventional video, the increasingly popular 360 virtual reality (VR) video requires copyright protection mechanisms. The classic approach for copyright protection is the introduction of a digital watermark into the video sequence. Due to the nature of spherical panorama, traditional watermarking schemes that are dedicated to planar media cannot work efficiently for 360 VR video. In this article, we propose a spherical wavelet watermarking scheme to accommodate 360 VR video. With our scheme, the watermark is first embedded into the spherical wavelet transform domain of the 360 VR video. The spherical geometry of the 360 VR video is used as the host space for the watermark so that the proposed watermarking scheme is compatible with the multiple projection formats of 360 VR video. Second, the just noticeable difference model, suitable for head-mounted displays (HMDs), is used to control the imperceptibility of the watermark on the viewport. Third, besides detecting the watermark from the spherical projection, the proposed watermarking scheme also supports detecting watermarks robustly from the viewport projection. The watermark in the spherical domain can protect not only the 360 VR video but also its corresponding viewports. The experimental results show that the embedded watermarks are reliably extracted both from the spherical and the viewport projections of the 360 VR video, and the robustness of the proposed scheme to various copyright attacks is significantly better than that of the competing planar-domain approaches when detecting the watermark from viewport projection.

A Molecular Stereostructure Descriptor Based On Spherical Projection

Description of molecular stereostructure is critical for the machine learning prediction of asymmetric catalysis. Herein we report a spherical projection descriptor of molecular stereostructure (SPMS), which allows precise representation of the molecular van der Waals (vdW) surface. The key features of SPMS descriptor are presented using the examples of chiral phosphoric acid, and the machine learning application is demonstrated in Denmark’s dataset of asymmetric thiol addition to N-acylimines. In addition, SPMS descriptor also offers a color-coded diagram that provides straightforward chemical interpretation of the steric environment.

The Centroid Solid Angle and Probability Models of Square Prism Dice Rolls

Past studies have indicated that the centroid solid angle is related to probabilities of square prism dice rolls. We explain how it is relevant to these probabilities and how to use the spherical projection to calculate the centroid solid angles for the faces on a square prism. These values are then used in a statistical analysis in the quest of constructing a mathematical probability model. The proposed model is based on the principle that the probability of ending up on a particular resting aspect is proportional to the centroid solid angle of that aspect and inversely proportional to a power of the centroid height in that aspect. Using a power of 2.427, this proposed model fits our data of over 60,000 non-symmetrical square prism dice rolls of various sizes (unequal heights and widths) with the largest magnitude Z-score of 1.01. Different powers can potentially describe other situations; e.g. different surfaces, larger dice, heavier dice, etc.

Research on the operational regional coverage of satellite and spacecraft tracking and controlling

In view of the problem of building ground stations for tracking and controlling of satellites and spacecraft, considering the fixed angle between the orbit of the satellite or spacecraft and the equatorial surface of the earth, and the difference of longitude between the two circles in succession of the satellite or spacecraft caused by the rotation of the earth, the operation area of the satellite or spacecraft was calculated by using the method of spherical projection of satellite orbit rotation, taking the earth as the reference system. The minimum number of ground stations needed for satellite tracking and controlling was calculated in three cases, by using the mathematical model of sphere ring area and honeycomb coverage. This model was validated by the launch and operation data of Shenzhou 7.