46 research outputs found
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
Video recognition systems are vulnerable to adversarial examples. Recent
studies show that style transfer-based and patch-based unrestricted
perturbations can effectively improve attack efficiency. These attacks,
however, face two main challenges: 1) Adding large stylized perturbations to
all pixels reduces the naturalness of the video and such perturbations can be
easily detected. 2) Patch-based video attacks are not extensible to targeted
attacks due to the limited search space of reinforcement learning that has been
widely used in video attacks recently. In this paper, we focus on the video
black-box setting and propose a novel attack framework named LogoStyleFool by
adding a stylized logo to the clean video. We separate the attack into three
stages: style reference selection, reinforcement-learning-based logo style
transfer, and perturbation optimization. We solve the first challenge by
scaling down the perturbation range to a regional logo, while the second
challenge is addressed by complementing an optimization stage after
reinforcement learning. Experimental results substantiate the overall
superiority of LogoStyleFool over three state-of-the-art patch-based attacks in
terms of attack performance and semantic preservation. Meanwhile, LogoStyleFool
still maintains its performance against two existing patch-based defense
methods. We believe that our research is beneficial in increasing the attention
of the security community to such subregional style transfer attacks.Comment: 14 pages, 3 figures. Accepted to AAAI 202
Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup
In recent years, various deep learning techniques have been exploited in side
channel attacks, with the anticipation of obtaining more appreciable attack
results. Most of them concentrate on improving network architectures or putting
forward novel algorithms, assuming that there are adequate profiling traces
available to train an appropriate neural network. However, in practical
scenarios, profiling traces are probably insufficient, which makes the network
learn deficiently and compromises attack performance.
In this paper, we investigate a kind of data augmentation technique, called
mixup, and first propose to exploit it in deep-learning based side channel
attacks, for the purpose of expanding the profiling set and facilitating the
chances of mounting a successful attack. We perform Correlation Power Analysis
for generated traces and original traces, and discover that there exists
consistency between them regarding leakage information. Our experiments show
that mixup is truly capable of enhancing attack performance especially for
insufficient profiling traces. Specifically, when the size of the training set
is decreased to 30% of the original set, mixup can significantly reduce
acquired attacking traces. We test three mixup parameter values and conclude
that generally all of them can bring about improvements. Besides, we compare
three leakage models and unexpectedly find that least significant bit model,
which is less frequently used in previous works, actually surpasses prevalent
identity model and hamming weight model in terms of attack results
Water-Soluble Electrospun Nanofibers as a Method for On-Chip Reagent Storage
This work demonstrates the ability to electrospin reagents into water-soluble nanofibers resulting in a stable on-chip enzyme storage format. Polyvinylpyrrolidone (PVP) nanofibers were spun with incorporation of the enzyme horseradish peroxidase (HRP). Scanning electron microscopy (SEM) of the spun nanofibers was used to confirm the non-woven structure which had an average diameter of 155 ± 34 nm. The HRP containing fibers were tested for their change in activity following electrospinning and during storage. A colorimetric assay was used to characterize the activity of HRP by reaction with the nanofiber mats in a microtiter plate and monitoring the change in absorption over time. Immediately following electrospinning, the activity peak for the HRP decreased by approximately 20%. After a storage study over 280 days, 40% of the activity remained. In addition to activity, the fibers were observed to solubilize in the microfluidic chamber. The chromogenic 3,3′,5,5′-tetramethylbenzidine solution reacted immediately with the fibers as they passed through a microfluidic channel. The ability to store enzymes and other reagents on-chip in a rapidly dispersible format could reduce the assay steps required of an operator to perform
Photovoltaic generator model for power system dynamic studies
Photovoltaic (PV) power generation has developed very rapidly worldwide in the recent years. There is a possibility that the PV power generation will switch from an auxiliary power supply, as of today, to a main power source in many power grids in the future. Naturally, dynamic studies on power grids with a high penetration of PV generators have become increasingly important, and thus have attracted major attentions from both the power industry and the academia. Consequently, dynamic modeling of PV generators has been investigated widely. However, among various proposed models, there is a confusion on the model applicability and a lack of the clarification on the required level of details on the modeling work, which severely limit the real industrial applications of the developed models. This paper reviews the state-of-the-art PV generator dynamic modeling work, with a focus on the modeling principles of PV generator for the power system dynamic studies. The paper presents the detailed modeling process for the recommended PV generator dynamic model, and clarifies the assumptions and simplifications made in the modeling process, thus raises the discussion on the model applicability. Studies that require further attentions on developing the dynamic models of PV generators for power system dynamic studies are identified and presented in the paper. However, this work does not intend to conclude the research work in this important field, instead, it aims to provoke more discussions on developing guidelines on building or selecting the appropriate models to fit into the purpose of the targeted dynamic studies
Operation characteristics of DC transmission system with large-scale renewable energy integration
Marked with flexible interconnection and control, the high-voltage direct current (HVDC) gird has captured much attention of industries and academics. Hybrid dual-infeed or multi-infeed HVDC composed of line-commutated-converter HVDC (LCC-HVDC) and voltage source converter HVDC (VSC-HVDC) will form the main pattern in a further power grid; meanwhile, the new gird pattern will bring new opportunities and challenges to security and stability control in the power system. First, research works on the control strategies and operation performances of LCC-HVDC and VSC-HVDC are stated in this paper; then, a model of wind power integration into a dual-infeed DC transmission system is established in PowerFactory, and case studies are conducted in both steady and transient states. On this basis, a new control strategy for variable-speed constant-frequency wind power generators to promote voltage characteristics of the DC network is designed in this paper, and two additional active power control segments are designed in the traditional control system; thus, DC voltage stability can be improved by fast regulation of active power output due to quick power adjustment of wind power generators; simulations are implemented and the results will lay a foundation for safe and stable operation in the DC transmission system with renewable energy integration
Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method
The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better
Structure–activity characteristics of phenylalanine analogs selectively transported by L-type amino acid transporter 1 (LAT1)
Abstract L-type amino acid transporter 1 (LAT1) is a transmembrane protein responsible for transporting large neutral amino acids. While numerous LAT1-targeted compound delivery for the brain and tumors have been investigated, their LAT1 selectivity often remains ambiguous despite high LAT1 affinity. This study assessed the LAT1 selectivity of phenylalanine (Phe) analogs, focusing on their structure–activity characteristics. We discovered that 2-iodo-l-phenylalanine (2-I-Phe), with an iodine substituent at position 2 in the benzene ring, markedly improves LAT1 affinity and selectivity compared to parent amino acid Phe, albeit at the cost of reduced transport velocity. l-Phenylglycine (Phg), one carbon shorter than Phe, was found to be a substrate for LAT1 with a lower affinity, exhibiting a low level of selectivity for LAT1 equivalent to Phe. Notably, (R)-2-amino-1,2,3,4-tetrahydro-2-naphthoic acid (bicyclic-Phe), with an α-methylene moiety akin to the α-methyl group in α-methyl-l-phenylalanine (α-methyl-Phe), a known LAT1-selective compound, showed similar LAT1 transport maximal velocity to α-methyl-Phe, but with higher LAT1 affinity and selectivity. In vivo studies revealed tumor-specific accumulation of bicyclic-Phe, underscoring the importance of LAT1-selectivity in targeted delivery. These findings emphasize the potential of bicyclic-Phe as a promising LAT1-selective component, providing a basis for the development of LAT1-targeting compounds based on its structural framework
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
Face recognition pipelines have been widely deployed in various
mission-critical systems in trust, equitable and responsible AI applications.
However, the emergence of adversarial attacks has threatened the security of
the entire recognition pipeline. Despite the sheer number of attack methods
proposed for crafting adversarial examples in both digital and physical forms,
it is never an easy task to assess the real threat level of different attacks
and obtain useful insight into the key risks confronted by face recognition
systems. Traditional attacks view imperceptibility as the most important
measurement to keep perturbations stealthy, while we suspect that industry
professionals may possess a different opinion. In this paper, we delve into
measuring the threat brought about by adversarial attacks from the perspectives
of the industry and the applications of face recognition. In contrast to widely
studied sophisticated attacks in the field, we propose an effective yet
easy-to-launch physical adversarial attack, named AdvColor, against black-box
face recognition pipelines in the physical world. AdvColor fools models in the
recognition pipeline via directly supplying printed photos of human faces to
the system under adversarial illuminations. Experimental results show that
physical AdvColor examples can achieve a fooling rate of more than 96% against
the anti-spoofing model and an overall attack success rate of 88% against the
face recognition pipeline. We also conduct a survey on the threats of
prevailing adversarial attacks, including AdvColor, to understand the gap
between the machine-measured and human-assessed threat levels of different
forms of adversarial attacks. The survey results surprisingly indicate that,
compared to deliberately launched imperceptible attacks, perceptible but
accessible attacks pose more lethal threats to real-world commercial systems of
face recognition.Comment: 19 pages, 12 figure
Construction of an example system for AC/DC hybrid power grid with high-proportion renewable energy
Large-scale wind and solar power generations have got rapid development in recent years in China and abroad. They always connect to load centres through high-voltage direct current (HVDC) transmission or ultra HVDC (UHVDC) transmission lines. Such systems are so large and it is necessary to make appropriate equivalence according to the purpose of different research. At present, the analysis model of this kind of real system is few, and simplified single-machine infinite-bus system or IEEE example system is adopted most of the time, which may affect the accuracy of analysis results. Here, the UHVDC project from a practical renewable energy base to load centres in China is chosen as an example, the actual structure and parameters are used to establish a typical example system for AC/DC hybrid power grid with high-proportion renewable energy. The new-generation synchronous condenser with large capacity and the supporting synchronous generators are all considered. First, the installed power generation, power grid structure, and load configuration of example system are introduced. Then the load flow distribution and transient stability characteristics are analysed by using DIgSILENT PowerFactory software. Finally, the feasibility of the proposed example system is shown by simulation results