Embedded real-time systems generally execute in a predictable and deterministic manner to deliver critical functionality within stringent timing constraints. However, the predictable execution behavior leaves the system vulnerable to schedule-based attacks. In this article, we present a multimode security-aware real-time scheduling scheme to counteract schedule-based attacks on multiprocessor real-time systems. To mitigate the vulnerability to the schedule-based attack, we propose a multimode scheduling method to reduce the accumulative attack effective window (AEW) of multiple victim tasks and prevent the untrusted tasks from executing during the AEW by distinctively scheduling mixed-trust tasks according to the system mode. To avoid the protection degradation due to the excessive blocking of untrusted tasks, we introduce a protection window for multiple victims on multiprocessors by analyzing the system protection capability limit under the system schedulability constraint. Furthermore, to maximize the protection capability of the multimode security-aware scheduling strategy on a multiprocessor platform, we also propose a security-aware packing algorithm to balance the workloads of mixed-trust tasks on different processors using a mixed-trust worst-fit decreasing heuristic strategy. The experimental results demonstrate that our proposed approach significantly outperforms the state-of-the-art method. Specifically, the AEW ratio and the AEW untrusted execution time ratio are reduced by 18.8% and 62.8%, respectively, while the defense success rate against ScheduLeak attack is improved by 16.3%.

Text detection in natural scene images is an important prerequisite for many content‐based multimedia understanding applications. The authors present a simple and effective text detection method in natural scene image. Firstly, MSERs are extracted by the V‐MSER algorithm from channels of G , H , S , O 1 , and O 2 , as component candidates. Since text is composed of character candidates, the authors design an MRF model to exploit the relationship between characters. Secondly, in order to filter out non‐text components, they design a set of two‐layers filtering scheme: most of the non‐text components can be filtered by the first layer of the filtering scheme; the second layer filtering scheme is an AdaBoost classifier, which is trained by the features of compactness, horizontal variance and vertical variance, and aspect ratio. Then, only four simple features are adopted to generate component pairs. Finally, according to the orientation similarity of the component pairs, component pairs which have roughly the same orientation are merged into text lines. The proposed method is evaluated on two public datasets: ICDAR 2011 and MSRA‐TD500. It achieves 82.94 and 75% F ‐measure, respectively. Especially, the experimental results, on their URMQ_LHASA‐TD220 dataset which contains 220 images for multi‐orientation and multi‐language text lines evaluation, show that the proposed method is general for detecting scene text lines in different languages.