Bifurcation and dynamic response analysis of rotating blade excited by upstream vortices

Acknowledgements The authors acknowledge the projects supported by the National Basic Research Program of China (973 Project)(No. 2015CB057405) and the National Natural Science Foundation of China (No. 11372082) and the State Scholarship Fund of CSC. DW thanks for the hospitality of the University of Aberdeen.Peer reviewedPostprin

Chosen-plaintext attack of an image encryption scheme based on modified permutation-diffusion structure

Since the first appearance in Fridrich's design, the usage of permutation-diffusion structure for designing digital image cryptosystem has been receiving increasing research attention in the field of chaos-based cryptography. Recently, a novel chaotic Image Cipher using one round Modified Permutation-Diffusion pattern (ICMPD) was proposed. Unlike traditional permutation-diffusion structure, the permutation is operated on bit level instead of pixel level and the diffusion is operated on masked pixels, which are obtained by carrying out the classical affine cipher, instead of plain pixels in ICMPD. Following a \textit{divide-and-conquer strategy}, this paper reports that ICMPD can be compromised by a chosen-plaintext attack efficiently and the involved data complexity is linear to the size of the plain-image. Moreover, the relationship between the cryptographic kernel at the diffusion stage of ICMPD and modulo addition then XORing is explored thoroughly

Effect of thiosulfate on passivity and corrosion properties of stainless steels

The effect of thiosulfate on different aspects of pitting corrosion in chloride containing environments as well as the interaction between thiosulfate and stainless steels were studied in this research. Cyclic potentiodynamic polarization tests showed that the presence of thiosulfate hinders the repassivation of the pits. Preferential dissolution of ferrite phase was found on 2101. A statistical analysis of the metastable pitting events monitored by chronoamperometry revealed that thiosulfate promotes pit initiation and stabilizes the growth of metastable pits. Microstructure influenced the pitting corrosion behavior of 2101. The repassivation of metastable pits were closely related to the arrest of pit growth by phase boundaries. Mechanical scratch tests, designed to study the repassivation kinetics and interactions between a bare metal and thiosulfate, showed the effect of potential and microstructure on the growth of pitting in presence of thiosulfate. Interaction of alloying elements such as chromium, nickel, and molybdenum with thiosulfates were also evaluated. XPS showed formation of reduced sulfur species on the surface of pure alloying elements, and provided information regarding the overall corrosion process. Based on these results, a mechanism of pitting in the presence of thiosulfate was proposed.Ph.D

Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we present a survey on existing DNN IP protection works in terms of the above six attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. After that, we analyze the potential attacks on DNN IP protection methods from the aspects of model modifications, evasion attacks, and active attacks. Besides, a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Lastly, future research opportunities and challenges on DNN IP protection are presented

Detect and remove watermark in deep neural networks via generative adversarial networks

Deep neural networks (DNN) have achieved remarkable performance in various fields. However, training a DNN model from scratch requires a lot of computing resources and training data. It is difficult for most individual users to obtain such computing resources and training data. Model copyright infringement is an emerging problem in recent years. For instance, pre-trained models may be stolen or abuse by illegal users without the authorization of the model owner. Recently, many works on protecting the intellectual property of DNN models have been proposed. In these works, embedding watermarks into DNN based on backdoor is one of the widely used methods. However, when the DNN model is stolen, the backdoor-based watermark may face the risk of being detected and removed by an adversary. In this paper, we propose a scheme to detect and remove watermark in deep neural networks via generative adversarial networks (GAN). We demonstrate that the backdoor-based DNN watermarks are vulnerable to the proposed GAN-based watermark removal attack. The proposed attack method includes two phases. In the first phase, we use the GAN and few clean images to detect and reverse the watermark in the DNN model. In the second phase, we fine-tune the watermarked DNN based on the reversed backdoor images. Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack. In the meantime, the proposed attack hardly affects the model's performance. The test accuracy of the watermarked DNN on the MNIST and the CIFAR10 datasets drops by less than 1% and 3%, respectively

A framework to design interaction control of aerial slung load systems: transfer from existing flight control of under-actuated aerial vehicles

This paper establishes a framework within which interaction control is designed for the aerial slung load system composed of an underactuated aerial vehicle, a cable and a load. Instead of developing a new control law for the system, we propose the interaction control scheme by the controllers for under-actuated aerial systems. By selecting the deferentially flat output as the configuration, the equations of motion of the two systems are described in an identical form. The flight control task of the under-actuated aerial vehicle is thus converted into the control of the aerial slung load system. With the help of an admittance filter, the compliant trajectory is generated for the load subject to external interaction force. Moreover, the convergence of the whole system is proved by using the boundedness of the tracking error of vehicle attitude tracking as well as the estimation error of external force. Based on the developed theoretical results, an example is provided to illustrate the design algorithm of interaction controller for the aerial slung load via an existing flight controller directly. The correctness and applicability of the obtained results are demonstrated via the illustrative numerical example