In this tutorial, you will learn how to perform liveness detection with OpenCV. You will create a liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems. How do I spot real versus fake faces? Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smartphone that they could hold up to the camera responsible for performing face recognition (such as in the image at the top of this post).

A pillar box red electric train connects Paris, Verona and Grenada via Budapest's Liberty Bridge and on to Heidelberg Castle in a 120-hectare fantasy business park dreamt up by the Chinese billionaire Ren Zhengfei. Ren, 74, a former Red Army engineer who founded the telecommunications company Huawei in 1987 and still owns a 1.14% stake, asked the Japanese architect Kengo Kuma to recreate some of Europe's most historic cities. He hoped to inspire an army of 25,000 research and development staff to challenge Apple, Google and Samsung. While its US competitors keep their research facilities on lockdown to prevent corporate espionage (oft allegedly by the Chinese), Huawei is inviting the world's media into its labs and factories in an attempt to dispel the US government's claims that the privately held company is an arm of the Chinese state and that its technology could be used to hack into western governments. US politicians allege that Huawei's forthcoming 5G mobile phone networks could be hacked by Chinese spies to eavesdrop on sensitive phone calls, gain access to counter-terrorist operations – and potentially even kill targets by crashing driverless cars.

Uber's self-driving car unit has been valued at $7.3bn (£5.6bn), after receiving $1bn of investment by a consortium including Toyota and Saudi Arabia's sovereign wealth fund. With weeks to go until the loss-making San Francisco firm's stock market float, expected to value the company at up to $100bn, Uber said it had secured new financial backing for its plans to develop autonomous vehicles. Japanese carmakers Toyota and its compatriot Denso, a car parts supplier, will invest a combined $667m in Uber's Advanced Technologies Group (ATG). The remainder will come from Japanese conglomerate SoftBank's $100bn Vision Fund, whose largest investor is Saudi Arabia. Toyota and SoftBank are already major investors in Uber, with the latter owning 16%.

SHANGHAI - Global automakers are positioning themselves for a brave new world of on-demand transport that will require a car of the future -- hyper-connected, autonomous and shared -- and China may become the concept's laboratory. With ride-hailing services booming and car-sharing not far behind, the need for vehicles tailored to these and other evolving mobility solutions is one of the hottest topics among global automakers gathered for this week's Shanghai Auto Show. Nearly all agree that there is no better proving ground than China: Its gigantic cities are desperate for answers to gridlock and its population is noted for its ready embrace of new high-tech services. To take advantage of this, manufacturers are competing not only to sell conventional and electric vehicles in the world's biggest auto market, but also to develop new technologies and even specific interiors designed for the on-demand world. "We cannot just develop electric cars. They will have to be smart, interconnected and of course shared," Zhao Guoqing, vice president of Chinese auto giant Great Wall Motors, said on the auto show's sidelines.

Nippon Telegraph and Telephone Corp. (NTT) said Thursday it will launch a trial for a farming support service using drones and artificial intelligence technology, with a goal of commercializing the service in Japan and other Asian countries. The new system, which connects drones with GPS satellites, is anticipated to help the farm industry in the nation amid a serious labor shortage. NTT aims to raise crop output by up to 30 percent through the new service. The telecommunications giant will conduct the trial service on 8 hectares of a rice field in Fukushima Prefecture from later this month to March 2021. It aims to launch the service on a commercial basis in Japan in two years.

Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration (e.g., large/delicate eyebrows). In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial regions and ignore low-level details. More specifically, we first design a cycle-like architecture to enable training with unpaired data. Then we design three losses from different aspects. A total variation loss is used to highlight important edges and contents in cartoon portraits. An attentive cycle loss is added to lay more emphasis on delicate facial areas such as eyes. In addition, a perceptual loss is included to eliminate artifacts and improve robustness of our method. Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods.

LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB. Most of the available libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. So LIBS2ML is completely unique due to its focus on the scalable second order methods, the hot research topic, and being based on MEX files. Thus it provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library. The library can be downloaded from the URL: \url{https://github.com/jmdvinodjmd/LIBS2ML}.

The adaptive moment estimation algorithm Adam (Kingma and Ba) is a popular optimizer in the training of deep neural networks. However, Reddi et al. have recently shown that the convergence proof of Adam is problematic and proposed a variant of Adam called AMSGrad as a fix. In this paper, we show that the convergence proof of AMSGrad is also problematic. Concretely, the problem in the convergence proof of AMSGrad is in handling the hyper-parameters, treating them as equal while they are not. This is also the neglected issue in the convergence proof of Adam. We provide an explicit counter-example of a simple convex optimization setting to show this neglected issue. Depending on manipulating the hyper-parameters, we present various fixes for this issue. We provide a new convergence proof for AMSGrad as the first fix. We also propose a new version of AMSGrad called AdamX as another fix. Our experiments on the benchmark dataset also support our theoretical results.

Applied Intelligence manuscript No. (will be inserted by the editor) Abstract The widespread adoption of smartphones dramaticallygenerated by our attacks models when used to harden increases the risk of attacks and the spread the developed anti-malware system improves the detection of mobile malware, especially on the Android platform. Machine learning based solutions have been already Keywords Adversarial machine learning · malware used as a tool to supersede signature based anti-malware detection · poison attacks · adversarial example · systems. However, malware authors leverage attributes jacobian algorithm. Hence, to evaluate the vulnerability of machine 1 Introduction learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications Nowadays using the Android application is very popular (apps). Every Android application inappropriately fits discriminant function on has a Jar-like APK format and is an archive file which the set of data points, eventually yielding a higher misclassification contains Android manifest and Classes.dex Further, to distinguish the adversarial manifest file holds information about the application examples from benign samples, we propose two defense structure and each part responsible for certain actions. To validate our For instance, the requested permissions must be accepted attacks and solutions, we test our model on three different by the users for successful installation of applications. We also test our methods The manifest file contains a list of hardware using various classifier algorithms and compare them components and permissions required by each application. Promising results show that generated the manifest file that are useful for running applications. Additionally, evasive variants is saved as the classes.dex In a nutshell, the by presenting some adversary-aware approaches?generated malware sample is statistically identical to a Do we require retraining of the current ML model to designbenign sample. To do so, adversaries adopt adversarial adversary-aware learning algorithms? How to properlymachine learning algorithms (AML) to design an example test and validate the countermeasure solutions inset called poison data which is used to fool machine a real-world network? The goal of this paper is to shedlearning models.

When Uber publicly filed for an initial public offering last week, it cemented its reputation as a technology behemoth with more than a few liabilities. One particularly weighty albatross: its Autonomous Technology Group, which since 2015 has poured hundreds of millions into building self-driving car tech it has yet to commercialize. Make that at least $1 billion: According to the filing, Uber spent $457 million in 2018 on research and development for autonomous vehicles (and its other tech moonshots, like "flying taxis")--a figure up 19 percent from 2017. So it was good news for Uber--not to mention the potential shareholders circling its IPO--when it announced a major investment into its Autonomous Technology Group from a Japanese consortium on Thursday. The $1 billion infusion comes from Toyota, the automotive supplier Denso, and the Softbank Vision Fund, which is aggressively bankrolling ambitious transportation technology companies.