From A to Z: Projective coordinates leakage in the wild

At EUROCRYPT 2004, Naccache et al. showed that the projective coordinates representation of the resulting point of an elliptic curve scalar multiplication potentially allows to recover some bits of the scalar. However, this attack has received little attention by the scientific community, and the status of deployed mitigations to prevent it in widely adopted cryptography libraries is unknown. In this paper, we aim to fill this gap, by analyzing several cryptography libraries in this context. To demonstrate the applicability of the attack, we use a side-channel attack to exploit this vulnerability within libgcrypt in the context of ECDSA. To the best of our knowledge, this is the first practical attack instance. It targets the insecure binary extended Euclidean algorithm implementation using a microarchitectural side-channel attack that allows recovering the projective representation of the output point of scalar multiplication during ECDSA signature generation. We captured 100k traces to estimate the number of traces an attacker would need to compromise the libgcrypt ECDSA implementation, resulting in less than 2k for commonly used elliptic curve secp256r1, demonstrating the attack feasibility. During exploitation, we found two additional vulnerabilities. However, we remark the purpose of this paper is not merely exploiting a library but about providing an analysis on the projective coordinates vulnerability status in widely deployed open-source libraries, filling a gap between its original description in the academic literature and the adoption of countermeasures to thwart it in real-world applications.

Mask R-CNN with data augmentation for food detection and recognition

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics<br>

Mask R-CNN with data augmentation for food detection and recognition

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics<br>