This article proposes a methodological framework for quantitative estimations of height and weight using behavioral data collected from smart devices. We analyze the connections between height and weight information and behavioral data from three aspects: walking speed, stride length, and step frequency and then extract two kinds of motion features including basic kinematic features and advanced features which use statistical measurements summarizing the dynamics of walking behavior over time and relative intensity of walking speed change, energy cost of one step during walking, and walking frequency, respectively, to describe the motion behavior. After that, we qualitatively and quantitatively analyze the complementarity of different motion data sources and show that more useful information existed in multisource motion data than that of only one motion data source. Based on this, we propose a feature fusion approach named Serial+CC to dealing with the relationships between all motion features from multiple smart devices and user traits of height and weight and then a fused feature set with high discrimination and low complexity is constructed. Finally, five regression models of SVM, BP neural networks, Random Forest, LSTM, and BiLSTM are built with the fused feature set. Empirical evaluations were performed on a dataset collected from 56 subjects. The results demonstrate that motion data collected from smart devices can be used for height and weight quantitative estimation. The results also illustrate our method of using motion data collected from multiple smart devices can achieve better performance than those of only using one smart devices. The best performance is achieved with average errors of 0.95% (1.59 cm) and 4.75% (2.90 kg) for height and weight estimations, respectively, in the scenario of using multiple devices.

Extensive investigations have revealed that the gait recognition system is always vulnerable to impersonation attacks, which pose significant threats to the identity access security. Previous impersonation strategies have primarily focused on mimicking the victim’s walking style or probing the similar gait features to merely manipulate the input samples, without concurrently undermining the built-in model of the gait recognizer, thereby failing to achieve cost-effective attacks. In contrast to these existing heuristic approaches, we propose an optimal adversarial complicity strategy, called collusion attack, which leverages the tight collaboration between an external attacker and an internal spy to tie up into the close colluder, simultaneously enabling the input-&model-corrupted tampering modes and misleading the gait recognizer more powerfully and stealthily for misidentifying the illegitimate Alice as legitimate Bob. Specifically, we formulate a bilevel optimization problem to model such a leader-follower Stackelberg game with sequentially adversarial interaction process between the colluders and gait recognizer. Further, to solve this challenging bilevel problem efficiently, we absorb the Lagrangian dual theory and linearization representation method to reformulate a tractable mixed integer program. Finally, we perform comparison and ablation experiments with the state-of-the-art attack modes on single-&multi-source gait datasets to verify the validity of our collusion strategy in inducing the mistaken identity with great success rate, high confidence, and low cost. Empirical results also shed light on key insights in mitigating the collusion attacks and enhancing the gait recognition robustness to safeguard the identity access applications.

Extensive studies have revealed that the prevalent deep neural networks (DNNs) are vulnerable to adversarial examples in image recognition tasks. However, previous adversarial example attacks always work in either the global semantic space or local semantic attributes, resulting that these attacks may violate the sophisticated attackers’ least-effort intentions, whereas adversarial perturbations due to the explicit semantic variations are probably perceived by human vision. In this paper, we propose a two-phase optimization modeling framework to devise a novel Critical Semantic Fusion guided least-effort Adversarial example attack (CSFAdv). Specifically, the first phase fuses the coarse-&fine-grained semantic maps to localize the latent critical semantic attention region (CSAR) from genuine image. Under the friendly guidance of CSAR-feasibility, the second phase absorbs the ReLU-penalization,L0-regularization andL∞-limitation to formulate a Top-1&Top-2 misclassification optimization problem, which can characterize the holistic least-effort tampering behaviors embodied in localizing the most critical semantic space, doctoring the least amounts of pixels, injecting the limited amplitudes of perturbations and launching the most readily adversarial attacks. Further, to solve this NP-hard problem mildly, we adapt the gradient renewal by means of merging the momentum (past gradient), present gradient and Hessian (future gradient) to formalize a generalized gradient descent algorithm for generating an optimal adversarial image. Finally, we perform numerical experiments to verify the validity of our CSFAdv against seven types of DNN-based image classifiers on three public ImageNet, MNIST and CIFAR10. Empirical illustrations from ten evaluations indices shed light on the superiority of CSFAdv over eight kinds of state-of-the-art attacks and also offer key clues in reinforcing the DNNs’ robustness.

Extensive studies have revealed that smart grid is vulnerable to the cyber-physical attacks. However, these strategies only focus on the cascading initiation phase to induce single-stage failures with multiple branch tripping, lacking of exploring the attack effectiveness in the propagation phase so that the deeply hidden cascading failures are underestimated. In this paper, we propose a novel false load injection attack strategy that can intentionally penetrate into the cascading propagation phase to drive the multi-stage dynamical failures with a cascading process. Specifically, we formulate a bi-level optimization problem to model the adversarial game between the operator and the attacker. The former is in charge of security-constrained economic dispatching to minimize the generation cost, and the latter aims to maximize the cumulative number of tripped branches. Further, we reformulate this NP-hard bi-level problem as a mixed integer linear program for tractable computation. Finally, we perform numerical simulations on different-scale IEEE test systems to validate our strategy in driving the dynamical failures.

Increasing integration of the information communication technology introduces security vulnerabilities into smart grid. False data injection attack can exploit the cyber vulnerabilities to compromise the physical grid without being detected. However, the existing attack strategies only consider the electrical characteristic of power flow calculation to induce multiple overloaded branches. On this basis, our attack strategy absorbs the topological adjacency of chain subgraph to induce the overloaded branch chains, which can better characterize the sequential propagation of cascading failures. To this end, a game-theoretical bilevel optimization problem is formulated to model the decision-making interaction between the upper-level attacker and the lower-level security constrained economic dispatching system. The simulation results in IEEE test systems verify that our attack strategy can induce the overloaded branch chains and justify that the overloaded branch chains are more effective than the overloaded branches in triggering the cascading failures.