23 research outputs found
The Synthesis Of Graphene Films Via Graphene Oxide Reduction Using Green Tea
In recent years, graphene has emerged as the most promising nanomaterial for various potential applications especially in biomedical field owing to its unique two dimensional (2D) nanostructure and intriguing physicochemical properties. A simple method to produce graphene was developed by reducing graphene oxide (GO) using green tea polyphenol (GTP) in a batch reactor. The aforementioned method was non-detrimental to the environment, cost effective and scalable for high-volume production. The product of the reduction process was referred as reduced GO (RGO). The effects of weight ratio of GTP/GO and reaction temperature on the reduction of GO were examined in details. The ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform infrared (FTIR), thermogravimetric analysis (TGA) and the measurement of zeta potential as well as the electrophoretic mobility reveal that a successful reduction of GO and the preparation of stable RGO dispersion in aqueous media could be attained by performing the reduction reaction of GO with GTP at 90 ºC using a weight ratio of GTP/GO=1. In addition, the UV-Vis spectroscopy and X-ray photoelectron spectroscopy (XPS) analysis show that the RGO prepared using GTP exhibits final position of absorption peak (271 nm) and intensity of sp2 carbon that almost similar to the RGO produced using hydrazine (N2H4) solution. This observation indicates that the effective reduction property of GTP as compared to the N2H4 solution as a standard reducing agent
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks
Spiking Neural Networks (SNNs) claim to present many advantages in terms of
biological plausibility and energy efficiency compared to standard Deep Neural
Networks (DNNs). Recent works have shown that DNNs are vulnerable to
adversarial attacks, i.e., small perturbations added to the input data can lead
to targeted or random misclassifications. In this paper, we aim at
investigating the key research question: ``Are SNNs secure?'' Towards this, we
perform a comparative study of the security vulnerabilities in SNNs and DNNs
w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack
methodology, i.e., without the knowledge of the internal structure of the SNN,
which employs a greedy heuristic to automatically generate imperceptible and
robust adversarial examples (i.e., attack images) for the given SNN. We perform
an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN
having the same number of layers and neurons (to obtain a fair comparison), in
order to study the efficiency of our methodology and to understand the
differences between SNNs and DNNs w.r.t. the adversarial examples. Our work
opens new avenues of research towards the robustness of the SNNs, considering
their similarities to the human brain's functionality.Comment: Accepted for publication at the 2020 International Joint Conference
on Neural Networks (IJCNN
Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference
The exponential increase in dependencies between the cyber and physical world
leads to an enormous amount of data which must be efficiently processed and
stored. Therefore, computing paradigms are evolving towards machine learning
(ML)-based systems because of their ability to efficiently and accurately
process the enormous amount of data. Although ML-based solutions address the
efficient computing requirements of big data, they introduce (new) security
vulnerabilities into the systems, which cannot be addressed by traditional
monitoring-based security measures. Therefore, this paper first presents a
brief overview of various security threats in machine learning, their
respective threat models and associated research challenges to develop robust
security measures. To illustrate the security vulnerabilities of ML during
training, inferencing and hardware implementation, we demonstrate some key
security threats on ML using LeNet and VGGNet for MNIST and German Traffic Sign
Recognition Benchmarks (GTSRB), respectively. Moreover, based on the security
analysis of ML-training, we also propose an attack that has a very less impact
on the inference accuracy. Towards the end, we highlight the associated
research challenges in developing security measures and provide a brief
overview of the techniques used to mitigate such security threats
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Adversarial examples have emerged as a significant threat to machine learning
algorithms, especially to the convolutional neural networks (CNNs). In this
paper, we propose two quantization-based defense mechanisms, Constant
Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness
of CNNs against adversarial examples. CQ quantizes input pixel intensities
based on a "fixed" number of quantization levels, while in TQ, the quantization
levels are "iteratively learned during the training phase", thereby providing a
stronger defense mechanism. We apply the proposed techniques on undefended CNNs
against different state-of-the-art adversarial attacks from the open-source
\textit{Cleverhans} library. The experimental results demonstrate 50%-96% and
10%-50% increase in the classification accuracy of the perturbed images
generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly
used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) -
Softmax()) available in \textit{Cleverhans} library