Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

SR‐tree: An index structure for nearest‐neighbor searching of high‐dimensional point data

Abstract: Similarity search methods using feature vectors are employed widely for implementation of content-based retrieval of visual data, and appropriate index structures were explored to accelerate the search. Methods proposed hitherto have used the R*-tree and the SS-tree. This study offers a faster index structure namely, the SR-tree (sphere/rectangle-tree). The main feature of the proposed method is that both bounding spheres and bounding rectangles are used in combination. Bounding spheres and rectangles have been already employed in the SS-tree and the R*-tree, respectively. Experiments carried out as a part of the present study show, however, that as dimensionality grows high, both methods become problematic. Thus, when using bounding rectangles, the difference between the length of the edge of the rectangle and the diagonal becomes too large; with bounding spheres, the volume increases considerably as compared to rectangles. On the other hand, the SR-tree method uses both spheres and triangles, which ensures more efficient partitioning as compared to the SS-tree or R*-tree. Evaluation experiments proved that the proposed method outperforms both SS-tree and R*-tree in terms of CPU time and number of disk accesses. © 1998 Scripta Technica, Syst Comp Jpn, 29(6): 59–73, 1998

Information Bounds and Convergence Rates for Side-Channel Security Evaluators

Abstract: Current side-channel evaluation methodologies exhibit a gap between inefficient tools offering strong theoretical guarantees and efficient tools only offering heuristic (sometimes case-specific) guarantees. Profiled attacks based on the empirical leakage distribution correspond to the first category. Bronchain et al. showed at Crypto 2019 that they allow bounding the worst-case security level of an implementation, but the bounds become loose as the leakage dimensionality increases. Template attacks and machine learning models are examples of the second category. In view of the increasing popularity of such parametric tools in the literature, a natural question is whether the information they can extract can be bounded.In this paper, we first show that a metric conjectured to be useful for this purpose, the hypothetical information, does not offer such a general bound. It only does when the assumptions exploited by a parametric model match the true leakage distribution. We therefore introduce a new metric, the training information, that provides the guarantees that were conjectured for the hypothetical information for practically-relevant models. We next initiate a study of the convergence rates of profiled side-channel distinguishers which clarifies, to the best of our knowledge for the first time, the parameters that influence the complexity of a profiling. On the one hand, the latter has practical consequences for evaluators as it can guide them in choosing the appropriate modeling tool depending on the implementation (e.g., protected or not) and contexts (e.g., granting them access to the countermeasures’ randomness or not). It also allows anticipating the amount of measurements needed to guarantee a sufficient model quality. On the other hand, our results connect and exhibit differences between side-channel analysis and statistical learning theory.

Ghostformer: A GhostNet-Based Two-Stage Transformer for Small Object Detection

Abstract: In this paper, we propose a novel two-stage transformer with GhostNet, which improves the performance of the small object detection task. Specifically, based on the original Deformable Transformers for End-to-End Object Detection (deformable DETR), we chose GhostNet as the backbone to extract features, since it is better suited for an efficient feature extraction. Furthermore, at the target detection stage, we selected the 300 best bounding box results as regional proposals, which were subsequently set as primary object queries of the decoder layer. Finally, in the decoder layer, we optimized and modified the queries to increase the target accuracy. In order to validate the performance of the proposed model, we adopted a widely used COCO 2017 dataset. Extensive experiments demonstrated that the proposed scheme yielded a higher average precision (AP) score in detecting small objects than the existing deformable DETR model.