Connect with us

Hi, what are you looking for?

Articles

Adversarial AI and the dystopian future of tech

AI

AI is a rapidly growing technology that brings many benefits to society. However, like all new technology, there are potential risks of misuse. Adversarial AI attacks are the most concerning potential exploits of AI. Enemy AI attacks use AI to maliciously manipulate or deceive another AI system. Most AI programs learn, adapt, and evolve through behavioral learning. This leaves room for anyone to teach AI algorithms malicious behavior, ultimately leading to controversial consequences and thus making them vulnerable to abuse. Cybercriminals and threat actors may exploit this vulnerability for malicious purposes and intent.

So far, most of the enemy’s attacks have been on researchers and laboratories, but concerns are growing. The emergence of adversarial attacks against AI or machine learning algorithms reveals deep cracks in AI mechanisms. The presence of such vulnerabilities in AI systems slows down the growth and development of AI and can become a significant security risk for those who use AI-integrated systems. Therefore, understanding and defending against adversarial AI attacks is critical to maximizing the potential of AI systems and algorithms.

Understanding Enemy AI Attacks

The modern world we live in today is heavily influenced by AI, but it has yet to completely conquer the world. Since its emergence, AI has come under ethical criticism, and there has been a general reluctance to fully adopt AI. However, growing concerns that vulnerabilities in machine learning models and AI algorithms could be part of the malice are a major impediment to the growth of AI/ML.

The basic similarities of enemy attacks are essentially the same.
Manipulation of AI algorithms or ML models to produce malicious results. However, an enemy’s attack usually involves two things:

  • Addiction: ML models are fed with inaccurate or misinterpreted data and are tricked into making false predictions.
  • Pollution: ML models are fed maliciously crafted data to trick already trained models into performing malicious actions or predictions.

Either way, contamination can become a widespread problem. Because this technique involves malicious actors injecting or injecting negative information, these acts can quickly become a widespread problem with the help of other attacks. In contrast, addiction seems easy to control and prevent, as it requires internal work to provide a training data set. Using the Zero Trust security model and other network security protocols can help prevent these insider threats.

But defending your business against enemy threats can be difficult. Common online security issues can be easily mitigated using various tools such as home proxies, VPNs, and even anti-malware software, but adversary AI threats can circumvent these vulnerabilities. and these tools are too primitive to ensure security.

What threat does enemy AI pose?

AI is already well-integrated and an important part of key areas such as finance, healthcare, and transportation. Safety issues in these areas can be particularly dangerous to all human lives. AI is so well integrated into human life that the impact of enemy threats can wreak havoc on AI.

In 2018, a report from the Office of the Director of National Security highlighted multiple threats from adversarial machine learning. Among the threats mentioned in the report, one of the most pressing concerns was the potential for these attacks to compromise computer vision algorithms.

Research has found several examples of AI positioning so far. In one such study, the researchers added small changes, or “disturbances,” to images of pandas that were invisible to the naked eye. This change allowed the ML algorithm to identify the panda image as a gibbon image.

Similarly, another study points to possible AI contamination where attackers operated facial recognition cameras with infrared light. This action allowed these attacks to weaken accurate detection and impersonate others.

Additionally, you can detect hostile attacks when working with email spam filters. Because email spam filtering tools properly filter spam email by tracking specific words, attackers can use acceptable words or phrases to manipulate these tools to compromise recipients’ inboxes. can be accessed. So, looking at these examples and research, it’s easy to see the impact of adversarial AI attacks on the cyberthreat landscape:

  • Adversarial AI opens the possibility of rendering AI-based security tools such as phishing filters useless.
  • IoT devices are AI-based. Adversarial attacks on them could lead to large-scale hacking attempts.
  • AI tools tend to collect personal information. Attacks can manipulate these tools to reveal collected personal information.
  • AI is a part of the defense system. Adversarial attacks on defense tools can put national security in danger.
  • It can bring about a new variety of attacks that remain undetected.

It is ever more crucial to maintain security and vigilance against adversarial AI attacks.

Is there any prevention?

Given the potential for AI developments to make human life more manageable and more sophisticated, researchers are already developing various ways to protect systems from hostile AI. One of these methods is adversarial training. In this training, a machine learning algorithm is pre-trained on positioning and contamination trials by subjecting it to possible perturbations.

For computer vision algorithms, the algorithm incorporates images and their conflicts. For example, a car’s visual algorithm designed to identify stop signs learned all possible changes to stop signs, such as stickers, graffiti, and even missing letters. This algorithm correctly detects the phenomenon despite the actions of the attacker. However, this method is not foolproof as it is impossible to identify all possible iterations of an adversary’s attack.

The algorithm uses non-invasive image quality characteristics to distinguish between legitimate and contradictory inputs. This technique can ensure that conflicting machine learning importers and alternatives are neutralized before the classification information is reached. Another method of this type involves preprocessing and denoising, which automatically removes noise that may arise from the input.

Conclusion 

AI is widely used in modern society, but it is not yet widespread. Machine learning and AI have succeeded in expanding and even dominating some areas of our daily lives, but they still have a long way to go. Until researchers fully understand the potential of AI and machine learning, gaps remain in containing adversarial threats within AI technology. However, research on this subject is ongoing, mainly due to its importance for AI development and deployment.

 

Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

You May Also Like

Trending News

Multimodal generative AI is already here and now; it is no longer in the future. In recent months, generative AI models have become widely...

Infographics

Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat.

Featured News

Levi Ray & Shoup, Inc. (LRS) announced today that Shell plc (“Shell”) has selected the LRS® Enterprise Cloud Printing Service, a fully managed service provided by...

Featured News

The first Social Listening Solution from Digimind integrates two potent AI engines to give users a thorough view of their online presence. The combination...