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HIGHT is a lightweight block cipher which has been adopted as a standard block cipher. In this paper, we present a bit-level algebraic fault analysis (AFA) of HIGHT, where the faults are perturbed by a stealthy HT. The fault model in our attack assumes that the adversary is able to insert a HT that flips a specific bit of a certain intermediate word of the cipher once the HT is activated. The HT is realized by merely 4 registers and with an extremely low activation rate of about 0.000025. We show that the optimal location for inserting the designed HT can be efficiently determined by AFA in advance. Finally, a method is proposed to represent the cipher and the injected faults with a merged set of algebraic equations and the master key can be recovered by solving the merged equation system with an SAT solver. Our attack, which fully recovers the secret master key of the cipher in 12572.26 seconds, requires three times of activation on the designed HT. To the best of our knowledge, this is the first Trojan attack on HIGHT.

This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts. We further propose a neural architecture search framework, termed ZoomNAS, to promote both the accuracy and efficiency of whole-body pose estimation. ZoomNAS jointly searches the model architecture and the connections between different sub-modules, and automatically allocates computational complexity for searched sub-modules. To train and evaluate ZoomNAS, we introduce the first large-scale 2D human whole-body dataset, namely COCO-WholeBody V1.0, which annotates 133 keypoints for in-the-wild images. Extensive experiments demonstrate the effectiveness of ZoomNAS and the significance of COCO-WholeBody V1.0.