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Technology
AI technology Algorithm Applications Machine learning Neural Networks Technology

Deciphering the Impact of Neural Networks on Artificial Intelligence Evolution

 

Artificial intelligence (AI) has long been a frontier of innovation, pushing the boundaries of what machines can achieve. At the heart of AI's evolution lies the fascinating realm of neural networks, sophisticated systems inspired by the complex workings of the human brain. 

In this comprehensive exploration, we delve into the multifaceted landscape of neural networks, uncovering their pivotal role in shaping the future of artificial intelligence. Neural networks have emerged as the cornerstone of AI advancement, revolutionizing the way machines learn, adapt, and make decisions. 

Unlike traditional AI models constrained by rigid programming, neural networks possess the remarkable ability to glean insights from vast datasets through adaptive learning mechanisms. This paradigm shift has ushered in a new era of AI characterized by flexibility, intelligence, and innovation. 

At their core, neural networks mimic the interconnected neurons of the human brain, with layers of artificial nodes orchestrating information processing and decision-making. These networks come in various forms, from Feedforward Neural Networks (FNN) for basic tasks to complex architectures like Convolutional Neural Networks (CNN) for image recognition and Generative Adversarial Networks (GAN) for creative tasks. 

Each type offers unique capabilities, allowing AI systems to excel in diverse applications. One of the defining features of neural networks is their ability to adapt and learn from data patterns. Through techniques such as machine learning and deep learning, these systems can analyze complex datasets, identify intricate patterns, and make intelligent judgments without explicit programming. This adaptive learning capability empowers AI systems to continuously evolve and improve their performance over time, paving the way for unprecedented levels of sophistication. 

Despite their transformative potential, neural networks are not without challenges and ethical dilemmas. Issues such as algorithmic bias, opacity in decision-making processes, and data privacy concerns loom large, underscoring the need for responsible development and governance frameworks. By addressing these challenges head-on, we can ensure that AI advances in a manner that aligns with ethical principles and societal values. 

As we embark on this journey of exploration and innovation, it is essential to recognize the immense potential of neural networks to shape the future of artificial intelligence. By fostering a culture of responsible development, collaboration, and ethical stewardship, we can harness the full power of neural networks to tackle complex challenges, drive innovation, and enrich the human experience. 

The evolution of artificial intelligence is intricately intertwined with the transformative capabilities of neural networks. As these systems continue to evolve and mature, they hold the promise of unlocking new frontiers of innovation and discovery. By embracing responsible development practices and ethical guidelines, we can ensure that neural networks serve as catalysts for positive change, empowering AI to fulfill its potential as a force for good in the world.
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Technology
Adversarial attacks AI tools Artificial Intelligence Neural Networks Technology

AI Tools are Quite Susceptible to Targeted Attacks

 

Artificial intelligence tools are more susceptible to targeted attacks than previously anticipated, effectively forcing AI systems to make poor choices.

The term "adversarial attacks" refers to the manipulation of data being fed into an AI system in order to create confusion in the system. For example, someone might know that putting a specific type of sticker at a specific spot on a stop sign could effectively make the stop sign invisible to an AI system. Hackers can also install code on an X-ray machine that alters image data, leading an AI system to make inaccurate diagnoses. 

“For the most part, you can make all sorts of changes to a stop sign, and an AI that has been trained to identify stop signs will still know it’s a stop sign,” stated Tianfu Wu, coauthor of a paper on the new work and an associate professor of electrical and computer engineering at North Carolina State University. “However, if the AI has a vulnerability, and an attacker knows the vulnerability, the attacker could take advantage of the vulnerability and cause an accident.”

Wu and his colleagues' latest study aims to determine the prevalence of adversarial vulnerabilities in AI deep neural networks. They discover that the vulnerabilities are far more common than previously believed. 

What's more, we found that attackers can take advantage of these vulnerabilities to force the AI to interpret the data to be whatever they want. Using the stop sign as an example, you could trick the AI system into thinking the stop sign is a mailbox, a speed limit sign, a green light, and so on, simply by using slightly different stickers—or whatever the vulnerability is, Wu added. 

This is incredibly important, because if an AI system is not dependable against these sorts of attacks, you don't want to put the system into operational use—particularly for applications that can affect human lives.

The researchers created a piece of software called QuadAttacK to study the sensitivity of deep neural networks to adversarial attacks. The software may be used to detect adversarial flaws in any deep neural network. 

In general, if you have a trained AI system and test it with clean data, the AI system will behave as expected. QuadAttacK observes these activities to learn how the AI makes data-related judgements. This enables QuadAttacK to figure out how the data can be modified to trick the AI. QuadAttack then starts delivering altered data to the AI system to observe how it reacts. If QuadAttacK discovers a vulnerability, it can swiftly make the AI see whatever QuadAttacK desires. 

The researchers employed QuadAttacK to assess four deep neural networks in proof-of-concept testing: two convolutional neural networks (ResNet-50 and DenseNet-121) and two vision transformers (ViT-B and DEiT-S). These four networks were picked because they are widely used in AI systems across the globe. 

“We were surprised to find that all four of these networks were very vulnerable to adversarial attacks,” Wu stated. “We were particularly surprised at the extent to which we could fine-tune the attacks to make the networks see what we wanted them to see.” 

QuadAttacK has been made accessible by the research team so that the research community can use it to test neural networks for shortcomings. 
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Neural Networks
AI Artifical Inteliigence Hacking Machine learning malware Neural Networks

Researchers Embedded Malware into an AI's 'Neurons' and it Worked Scarily Well

 

According to a new study, as neural networks become more popularly used, they may become the next frontier for malware operations. 

The study published to the arXiv preprint site stated, malware may be implanted directly into the artificial neurons that make up machine learning models in a manner that protects them from being discovered.

The neural network would even be able to carry on with its usual activities. The authors from the University of the Chinese Academy of Sciences wrote, "As neural networks become more widely used, this method will become universal in delivering malware in the future." 

With actual malware samples, they discovered that changing up to half of the neurons in the AlexNet model—a benchmark-setting classic in the AI field—kept the model's accuracy rate over 93.1 percent. The scientists determined that utilizing a method known as steganography, a 178MB AlexNet model may include up to 36.9MB of malware buried in its structure without being detected. The malware was not identified in some of the models when they were tested against 58 different antivirus programs. 

Other ways of invading businesses or organizations, such as attaching malware to papers or files, are frequently unable to distribute harmful software in large quantities without being discovered. As per the study, this is because AlexNet (like many machine learning models) is comprised mainly of millions of parameters and numerous complicated layers of neurons, including fully connected "hidden" layers, 

The researchers discovered that altering certain other neurons had no influence on performance since the massive hidden layers in AlexNet were still intact. 

The authors set out a playbook for how a hacker could create a malware-loaded machine learning model and distribute it in the wild: "First, the attacker needs to design the neural network. To ensure more malware can be embedded, the attacker can introduce more neurons. Then the attacker needs to train the network with the prepared dataset to get a well-performed model. If there are suitable well-trained models, the attacker can choose to use the existing models. After that, the attacker selects the best layer and embeds the malware. After embedding malware, the attacker needs to evaluate the model’s performance to ensure the loss is acceptable. If the loss on the model is beyond an acceptable range, the attacker needs to retrain the model with the dataset to gain higher performance. Once the model is prepared, the attacker can publish it on public repositories or other places using methods like supply chain pollution, etc." 

According to the article, when malware is incorporated into the network's neurons, it is "disassembled" and assembled into working malware by a malicious receiver software, which may also be used to download the poisoned model via an upgrade.  The virus can still be halted if the target device checks the model before executing it. Traditional approaches like static and dynamic analysis can also be used to identify it.

Dr. Lukasz Olejnik, a cybersecurity expert and consultant, told Motherboard, “Today it would not be simple to detect it by antivirus software, but this is only because nobody is looking in there.” 

"But it's also a problem because custom methods to extract malware from the [deep neural network] model means that the targeted systems may already be under attacker control. But if the target hosts are already under attacker control, there's a reduced need to hide extra malware." 

"While this is legitimate and good research, I do not think that hiding whole malware in the DNN model offers much to the attacker,” he added. 

The researchers anticipated that this would “provide a referenceable scenario for the protection on neural network-assisted attacks,” as per the paper. They did not respond to a request for comment from Motherboard.

This isn't the first time experts have looked at how malicious actors may manipulate neural networks, such as by presenting them with misleading pictures or installing backdoors that lead models to malfunction. If neural networks represent the future of hacking, major corporations may face a new threat as malware campaigns get more sophisticated. 

The paper notes, “With the popularity of AI, AI-assisted attacks will emerge and bring new challenges for computer security. Network attack and defense are interdependent. We hope the proposed scenario will contribute to future protection efforts.”
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