Intrusion of AI and deep neural networks opened new perspectives in detecting terrorism

Over the past few years, terrorist attacks have been throwing challenging dimensions to global security. Even though when international communities and nations across the world are trying to put an end to it, malicious groups still find a way out. However, emerging AI technologies make their part of the contribution to combat terrorism.

Extremism has drastically increased in recent years. Terrorists from across the borders entering India are often exploiting weaknesses in border infrastructure. Except for the remarkable 9/11 attack in the US, and Mumbai and Pulwama attack in India, there are a bunch of other small massacres that killed hundreds of people and security personnel. Nations like Afghanistan and Syria are a constant resort for terrorist’s anger. The incident of Taliban attacking a south district of Afghanistan named Helmand and killing more than a dozen people is the most contemporary state of terrorism in the region.

However, as the instigating controversies linger here and there in every nation, it is time for technology to take a head-on at the chaos. When we take India as a nation fighting against terrorism using technology, the country is at a moderate state and is doing fine.

AI Can Be a Solution for Raging Terrorism

AI is a tool that carries out functionalities encoded in them, which are inherited from the intelligence of their human programmers. However, with the correct set of data, these AI engines can be trained to learn to identify patterns and information which may be humanly impossible, especially while working with huge digital database moving at a high data rate.

The existing simple technologies have eased the information access of terrorists through surveillance cameras, satellite data, drones, wearables, manual analysis of such big and diverse data to extract strategic knowledge. But these are not enough to wipe out terrorism. Ultimately, the intrusion of AI and the breakthrough of deep neural networks opened a new perspective for providing such solutions with human-grade accuracy.

For India, terrorism is not a new threat. However, the continuous increase in terrorist attacks has triggered a quench for a technological solution. As the terrorists and organizations expand in the new era through technology by transmitting information on the web and collecting funds from cyber attacks, nations across the globe also embraced AI features to fight against extremism. Some of the popular tools used in the process are facial recognition, Artificial Neural Network (ANN) and social network analysis. Here are the some use cases

• Artificial Neural Network (ANN)- ANNwith a feed-forward backpropagation network is establishing a high degree of certainty in various deceptive behavior patterns. These help authorities to extensively identify terrorists.

• Social network analysis- This tool is particularly suitable for analyzing terrorist networks since they take relationships into account, rather than only attributes, which are otherwise difficult to obtain for covert networks.

• Facial recognition system- It identifies the whitelisted or blacklisted people by referring to the database of facial features of people stored in advance.

Here are some of the other AI tools that track down terrorists

Machine Learning: The prediction and analysis of big data using machine learning and predictive framework can be integrated with the GIS system to identify the exact location of the suspicious attempts by embarking the border of each region. Machine Learning can read the audio and video of each frame, segment into the relevant bucket and notify the one which gets into the red bucket. The analytics as a part of Machine Learning can not only predict the attack, can also respond to the attacks on a real-time basis. The AI platform can immediately trigger an alarm or notify the concerned official to take action on exigency.

Intelligent Transport Management System: The technology acts for the moving vehicles to track down the movement of terrorists. Intelligent transport management system triggers an alarm and notifies officials if a suspicious vehicle travels in the wrong direction. The technology also sends an alert when it detects a vehicle with a fake number plate.

Edge Analysis: Edge analysis processes the raw data to the insightful information at each of the junctions or edge notes. The data needn’t be diverting to the central computation system and thus eliminating the huge thirst on the bandwidth.

Various countries offer tech solutions for terror issues

India has established a wide range of surveillance and data gathering tools to keep a track on malicious activities. Through database-centric schemes like National Intelligence (NATGRID), Network Traffic Analysis Systems (CCTNS), law enforcement agencies have achieved a centralised Lawful Intercept and Monitoring (LIM) system.

Taranis drones are being developed in the UK to fight terrorism. The unmanned combat aerial vehicle is expected to be fully operational by 2030. The drones are capable of replacing the human-piloted Tornado GR4 fighter planes.

Other countries including the United States and Russia are progressing robotic tanks that can operate autonomously. It can also be remotely controlled. The US has announced the initiative for an autonomous warship in 2016. The sea vehicle is expected to have offensive capabilities including anti-submarine weaponry. South Korea uses a Samsung SGR-A1 sentry gun that is supposedly capable of firing autonomously to guard its border.

Social Media in Flagging Unusual Posts

Social media platforms have taken centre stage in the tech era. However, the echoing of fake news and misinformation is turning to be a threat to national security. Facebook has recently announced that the platform is using AI to find and remove terrorism-related content from social media. Facebook uses image recognition technology to identify and prevent photos and videos of known terrorists from circulating in other accounts. The company also suggested it could use machine-learning algorithms to look for patterns in terrorist propaganda, so it could more swiftly remove it from the newsfeeds of others. These anti-terror efforts would extend to other platforms Facebook owns including WhatsApp and Instagram. Facebook partnered with other tech companies including Twitter, Microsoft and YouTube to create an industry database that documents the digital fingerprints of terrorist organizations.

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Artificial Intelligence (AI) made leapfrogs of development and saw broader adoption across industry verticals when it introduced machine learning (ML). ML helps in learning the behavior of an entity using patterns detection and interpretation methods. However, despite its unlimited potential, the conundrum lies in how machine learning algorithms arrive at a decision in the first place. Questions like, “What are the processes they adopted, and at what speed? How did they make such autonomous decision?” often raises concern about reliability on ML models. Though it helps in parsing huge amounts of data into intelligent insights for applications ranging from fraud detection to weather forecasting, the human mind is constantly baffled how it achieves conclusions. Moreover, the recurrent need to comprehend the procedures behind the decisions becomes more crucial when there is a possibility that the ML model makes decisions based on incomplete, error-prone, or one-sided (biased) information that can put few gatherings inside the network at a disadvantage. Enter Explainable AI (XAI).

This discipline holds the key to unlocking the AI and ML black box. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the average human user can understand it. This user can be either a programmer, end-user, or person impacted by an AI model’s decisions. According to a research report in Science Direct, the earlier AI model systems were easily interpretable. For instance, decision trees, Bayesian classifiers, and other algorithms which possess certain amounts of traceability, visibility, and transparency in their decision making process. But since of late, AI saw the emergence of complex and opaque decision systems such as Deep Neural Networks (DNNs).

The empirical success of Deep Learning (DL) models such as DNNs stems from a combination of efficient ML algorithms and their huge parametric space. The latter space comprises hundreds of layers and millions of parameters, which makes DNNs be considered as complex black-box models.  The opposite of black-box-ness is transparency, i.e., the search for a direct understanding of the mechanism by which a model works. And recently, the demand for transparency has gained more traction. As mentioned earlier, this demand rose due to ethical concerns like the data set used to train ML systems may not be justifiable, legitimate, or that do not allow obtaining detailed explanations of their behavior. Besides, opaque black box AI (and ML) decision making, XAI, also addresses bias inherent to AI systems. Bias in AI can prove detrimental, especially in recruitment, healthcare, and law enforcement sector.

According to the US Defense Advanced Research Project Agency (DARPA), XAI constitutes on three basic concepts: accurate predictions, inspection, and traceability. Here accuracy in prediction refers to how models will explain conclusions are reached to improve future decision making, decision understanding, and trust from human users and operators. And, traceability empowers humans to get into AI decision loops and have the ability to stop or control their tasks whenever the need arises. This is why XAI is gaining more importance in the past couple of years. In a recent forecast, Forrester predicts demand surge for transparent and explainable AI models, citing that 45% of AI decision-makers say trusting the AI system is either challenging or very challenging.

Last year, IBM researchers open-sourced AI Explainability 360 to help developers to gain more explainable insights on ML models and their predictions. Even Google, too, has announced its new set of XAI tools for developers. And with public interest growing in AI and ML that is explainable and adheres to regulations like GDPR, enterprises will have no choice but adopt XAI tools that will remove the black box in AI algorithms, focusing on enhancing explainability, mitigating bias and creating better outcomes for all.

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Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data.

An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data.

In recent years, this type of attack has become increasingly common, highlighting the vulnerabilities and flaws of many deep neural networks. A specific type of adversarial attack that has emerged in recent years entails the addition of adversarial patches (e.g., logos) to images. This attack has so far primarily targeted models that are trained to detect objects or people in 2-D images.

Researchers at Texas A&M University, University of Texas at Austin, University of Science and Technology in China, and the MIT-IBM Watson AI Lab have recently introduced a new attack that entails the addition of 3-D adversarial logos to images with the aim of tricking deep neural networks for object detection. This attack, presented in a paper pre-published on arXiv, could be more applicable to real-world situations, as most real data processed by deep neural networks is in 3-D.

“The primary aim of this work is to generate a structured patch in an arbitrary shape (called a ‘logo’ by us), termed as a 3-D adversarial logo that, when appended to a 3-D human mesh and then rendered into 2-D images, can consistently fool the object detector under different human postures,” the researchers wrote in their paper.

Essentially, the researchers created an arbitrary shape logo based on a pre-existing 2-D texture image. Subsequently, they mapped this image onto a 3-D adversarial logo, employing a texture-mapping method known as logo transformation. The 3-D adversarial logo they crafted could then serve as an adversarial texture, allowing the attacker to easily manipulate its shape and position.

In contrast with previously introduced attacks that utilize adversarial patches, this new type of attack maps logos in 3-D, yet it derives its shapes from 2-D images. As a result, it enables the creation of versatile adversarial logos that are can trick a broad variety of object or person detection methods, including those used in real-world situations, such as techniques for identifying people in CCTV footage.

“We render 3-D meshes with the 3-D adversarial logo attached into 2-D scenarios and synthesize images that could fool the detector,” the researchers wrote in their paper. “The shape of our 3-D adversarial logo comes from the selected logo texture in the 2-D domain. Hence, we can perform versatile adversarial training with shape and position controlled.”

The researchers tested the success rate of their adversarial logo attack by implementing it on two state-of-the-art deep neural network-based object detectors, known as YOLOv2 and YOLOv3. In these evaluations, the 3-D adversarial logo fooled both detectors robustly, causing them to misclassify images taken from a variety of angles and in which humans were in different postures.

These results confirm the vulnerabilities of deep neural network-based techniques for detecting objects or humans in images. They thus further highlight the need to develop deep learning methods that are better at spotting adversarial images or logos and that are harder to fool using synthesized data.

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Yoshua Bengio is one of the foremost thinkers in a field within artificial intelligence known as artifical neural networks and deep learning. Although significant progress has been made in recent years due to (among other factors) the combination of the proliferation of data, the decreasing cost of compute, and the tremendous amount of money and talent now devoted to artificial intelligence, Bengio chose this as a field of study during the 1980s, in the throes of what some referred to as the AI winter, seeing through a period when money and enthusiasm for artificial intelligence had dried up.

Bengio is the co-author (with Ian Goodfellow and Aaron Courville) of Deep Learning, a book that Elon Musk referred to as “the definitive textbook on deep learning.” On top of his growing influence in this field, he has also been enormously influential in shaping Montreal to become a hotbed for artificial intelligence. Bengio co-founded Element AI in 2016, which has a stated mission to “turn the world’s leading AI research into transformative business applications.” Element AI aims to foster partnership between the private sector and academia to help push the expansion of AI.

Bengio believes Montreal has emerged as a powerhouse due to the combination of great universities, great companies (including a number of Silicon Valley companies who have established offices in Montreal), and Canada’s ethos of cooperation among elite minds. We cover all of the above and more herein.

Peter High: Where does the field of deep neural networks currently stand in your estimation?

Yoshua Bengio: We have made amazing progress, but we are far from human level intelligence with computers. Most of the progress has been with supervised learning, which means machines are taught by essentially imitating humans. With supervised learning, humans provide the high-level concepts that the computer learns, which can be tedious and limits the ability of computers to discover things by themselves. Unsupervised learning, or what we call reinforcement learning, is when the learner is not merely passively observing the world, or how humans do things, but interacts with the environment and gets feedback. Humans are good at this. Combining unsupervised deep learning and reinforcement learning is one of the things that I am working on.

High: What steps are needed to reach the more fully realized version of unsupervised learning?

Bengio: First, we have to understand what we have in front of us. That is how science works. It is not just about building new things. It is about understanding the algorithms and the phenomena that we are studying. If we look at the best current deep learning systems, the ones that are trained on millions, or even billions, of examples, the kinds of errors they make tell us a lot about their superficial understanding of the world, or the aspect of the world that they are trained on. Our findings and progress should not be seen as discouraging though because an animal’s understanding of the world is also superficial. As humans, we have a deeper understanding that allows us to survive better than animals and to trick animals into doing things, the same way we are now able to trick neural nets into producing the wrong answers.

How do we move forward? For more than a decade, my research has focused on the notion of learning better representations, which is the heart of deep learning, in particular, representations that have a property called disentangled. Disentangled separates the different concepts and different explanations – we call them factors – that explain the data, that explain what the agent sees around it, and that explain how the agent patrols the world. Disentangled captures some of the causality that explains what we are seeing and what the computer is seeing.

I do not yet know the exact steps that will get us where we want to go. I have a program, but there are a lot of unknowns. When researching, it is important to explore in many directions. That is the difference between basic research, which is long-term and exploratory, and applied research, which is when we take what we have and fine-tune it, so we can build products and solve concrete problems. Applied research is important because AI is greatly impacting the world, both commercially and through things like health care, education, and agriculture.

High: You recently co-founded a company called Element AI. What was the genesis of the enterprise and what is its mission?

Bengio: The genesis was a dream of building an international hub and a new ecosystem for AI in Montreal. To create a new ecosystem that includes both research and innovation elements, we needed great companies to be involved. One of the first companies is Element AI. It occupies an important niche in the ecosystem. There are large multinational companies that, unlike companies such as Google, Facebook, Amazon, Microsoft, IBM, and so on, do not have a team of scientists working on AI. They are rightfully concerned that unless they are helping to chart the course of AI within their organization, their business could be transformed in a direction that they do not want. Element AI helps these companies by developing spinoffs and business deals with them and by connecting them to local startups and companies.

Another important aspect of Element AI is that it is developing a network of internal and external researchers who conduct AI, machine learning, and deep learning research. This network of researchers within universities and Element AI is free to explore any new idea. They also collaborate with the applied researchers at Element AI so that they can be at the forefront of what is going on in the field for their applications. For instance, one concept that Element AI is working on is what we call transfer learning, which is the ability for a machine to improve learning on a new problem by taking advantage of what has been learned before on other datasets or other problems. This means we might not need as large of a dataset to get good results on new problems.

High: Are there certain companies or industries that are particularly well suited to be clients of Element AI?

Bengio: The response from industry, across all sectors, has been amazing. There is more demand than our current team can handle. A lot of what we are doing right now is recruiting new people. Currently, Element AI is not focused on one particular sector, but is exploring all of the possibilities and starting projects in many areas.

High: When an enterprise that is at the beginning of their artificial intelligence journey comes to Element AI, what general things do you advise them to do to begin to ramp up on these topics? What building blocks do you suggest?

Bengio: The first thing is to come up with a strategy that brings together the expertise from various aspects of the company. This includes the people who have the vision for the different products and where they could go in the future and the people who know what the market in this particular niche might be pulling and asking for. The data side is also clearly important. What data have been accumulated? What data could be collected? Finally, we have to examine whether it makes sense, from a deep learning point of view, to tackle these problems. Can we, in a reasonable amount of time, conceive of solutions that would potentially address these hard problems using foreseen data? There has to be a meeting of the minds with people from the organization who have different strengths and expertise with Element AI team members where we come up with a number of recommendations. For large companies, there could be a portfolio of potential projects that has to be prioritized and evaluated before an effort gets going.

High:  In addition to being an entrepreneur, you are a professor at the University of Montreal. This seems to mirror the two halves you described before. There is the longer-term, less immediate research you are undertaking, versus the shorter-term commercial implications of what you are doing with Element AI. How do you divide your time between the two efforts?

Bengio: My main job is academic. I am full time at the university. I advise Element AI and other companies. The work that I do with Element AI and companies like Microsoft, for example, is primarily about the long-term aspect. That is my strength and what I can offer. I am not into the nitty-gritty of the commercial things in a company.

I am also developing the Montreal Institute of Learning Algorithms [MILA]. It is like a big startup and has become the heart of the AI ecosystem in Montreal. MILA brings together the machine learning researchers of the University of Montreal and McGill University. We already have about 200 people involved. We expect to double the number of professors and students over the next two to three years.

High: In many ways, much of what you have discussed, with regards to your work at the University of Montreal, with MILA, and with Element AI is about building an ecosystem. Is that an accurate perception?

Bengio: The community aspect is essential. A lot of success is due to the particular culture that has been developed in my lab at MILA. Our culture puts people center stage. Researchers with our expertise are in such high demand that we have to treat them like gold. We give them the freedom to find their place in the community so that they can be as motivated as possible to contribute and create something with their talent. Also, the Quebec culture is less individualistic and competitive than the culture of much of North America. We are more likely to collaborate and work together to build something.

High: Canada is doing interesting and progressive things to attract AI talent. You referred to your vision of making Montreal an artificial intelligence hub. Why has Montreal, in particular, been successful in this area? Is there something about the relationship between academics, the city or province governments, and the business community that has fostered the partnerships that are necessary to bring about an AI revolution?

Bengio: You listed some important factors, but I would say the essential factor is a simple one: critical mass. My group was one of the first to conduct deep learning research. It started with the University of Montreal trusting our vision and allowing us to recruit more professors, in a field that was not yet popular. Then there was a snowball effect. Better researchers brought in better students and better postdocs. This meant we were able to publish better papers, so we attracted more international visibility, which meant we could recruit even better people.

Similarly, when deep learning became more commercially interesting, more companies and investors were attracted to it. This accelerated. As large companies like Microsoft, Google, Facebook, and others came to Montreal, we became even more visible to the scientific and investor communities. All of this stimulated entrepreneurship. More of my students want to start companies, and they are doing it here. Investors used to ask our entrepreneurs to go to the Valley, but now they are happy with them staying in Canada.

As you mentioned, it is not just Montreal. Toronto is moving on a similarly fast track. We have created a collaboration between the Vector Institute in Toronto, the MILA in Montreal, and the Alberta Machine Intelligence Institute in Edmonton. We are forming the AI Consortium for Canada. Our goal is to put Canada on the map scientifically and as the country for AI. We are a small player compared to the U.S. and China, but our critical mass is making a big difference. Silicon Valley is not a big place in terms of the number of people, but it is a center for innovation because of the critical mass effect.

High: I want to turn to AI safety. Elon Musk has tweeted his concerns that AI may lead to World War III. He is not alone. A number of leading entrepreneurs and academics have highlighted the potential threats of artificial intelligence if it is not harnessed appropriately. Where do you stand on this?

Bengio: I am not worried about Terminator scenarios of AI taking over humanity. It is good that some people are investigating those issues, but they are far away. I am concerned with potential short and medium-term issues with the application of AI. AI can be misused. As you mentioned, one concern is use in the military sector. Within the scientific community, there is a lot of support for an international treaty that bans lethal autonomous weapons, or LAWs. Serious discussions are taking place, including in the U.N., about this issue. There are some countries, like the U.S., that are trying to block bans. That is a big mistake. Allowing LAWs to be easily commercialized is bad for everyone’s security because it will make it easier for small players to use these tools. It does make sense to conduct military research on defensive techniques against lethal autonomous weapons. However, we need an international treaty that stigmatizes the use of these weapons in the same way that, over the last two decades, we have succeeded in stigmatizing the development of nuclear weapons, chemical weapons, biological weapons, and landmines. Although we may not be able to perfectly and tidily prevent those things, if we can reduce them by making it clearly immoral for everyone, including countries and large companies, to develop them, we can greatly reduce the security risk for everyone on earth.

High: What near term areas of progress with deep learning will impact our everyday lives?

Bengio: The progress in deep learning for natural language is one area. As computers become better at understanding what is written and said, and can respond and generate natural language, the way we interact with computers will be transformed. That will likely have a big impact on the job market, both positively and negatively, and is therefore something that many companies are investing heavily in. This, in part, is why rapid progress is being made. Scientific progress is proportional to the effort that is put in. One reason deep learning has been so successful in the last three years is because there are many more people doing research and then developing things with it. That will continue.

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