The days when you could use a simplistic piece of code to automate or manipulate gameplay are long gone, at least when it comes to the most popular multiplayer games. Thanks to artificial intelligence, they can detect such unnatural player behavior and ban users almost instantly.
On the other hand, cheaters have become well aware of this development and decided to follow the trends.
Obviously, they started looking for ways to use AI to their advantage. As AI advances, it makes cheats in online games look more natural.
So, how did other players and game developers respond? By investing more in AI-powered anti-cheat solutions. Basically, we now have an unusual situation in which humans are not trying to outsmart each other - it’s AI vs AI. This trend is also reflected in different areas. On this page, we’ll focus on the history of this clash its current status and predict how it could evolve. Let’s begin!
The Moment Which Kicked Things Off
As noted, players have used simple codes to exploit video game vulnerabilities and improve their results since the 1990s. For instance, we have aimbots, which represent cheating scripts that automatically aim and shoot at opponents, leading to unnatural user accuracy and precision. Thanks to the wallhacks, players can see and/or shoot through walls and other objects, revealing hidden enemies.
Today, things are different. Cheaters train AI models to mimic their behavior. So, these AI models study their gameplay and simply upgrade it to make it more successful – not perfect, but way better.
You can tell this trend is gaining ground by checking software projects like RLCheats or “AimGod” at open-source repositories. You’ll notice that adaptive, machine-trained behavior rapidly substitutes the automation approach.
These models provide various AI cheats, such as memory injection, which modifies a game’s runtime data and manipulates variables like position, ammo, and player health. More sophisticated cheaters utilize external hardware and AI-driven computer vision for rapid game visual analysis, eliminating the need for any modifications. These are way harder to detect.
Modding forums are usually the best places to look for these tools. Some creators disguise these cheats as research and launch their products on the commercial market. For instance, in 2021, a Chinese gang was busted with $76 million in cheat subscription revenue.
Using AI to Counter-attack AI-based Cheat Systems
The leading anti-cheat systems used to protect gamers in popular video games, such as Valorant, Counter-Strike, and Rocket League, acted quickly and leveraged the latest technology to their advantage. Here are a few examples of their approach to battling AI-based cheat systems.
· Riot Vanguard. This kernel-level anti-cheat system, developed by Riot Games, relies on AI-driven behavioral analysis and machine learning to detect and prevent AI cheats, including wallhacks and aimbots, in Valorant.
· Anybrain.ai. This anti-cheat solution utilizes non-invasive AI to analyze over 70 gameplay parameters, including reaction times and mouse dynamics, to detect AI-assisted cheats, bots, and smurfs across various games.
· Valve VACnet. Valve uses this AI-based anti-cheat system for Counter-Strike. It leverages deep learning to analyze gameplay and identifies unnatural aiming and movement patterns.
· FACEIT Sentinel AI. The FACEIT platform has developed Sentinel AI, a system that combines client- and server-side AI checks to detect AI-driven cheats. Once again, this system relies on behavioral analysis.
Other Platforms Benefit Too
The iGaming industry falls under the broader category of online gaming, so it’s no wonder that both AI-facilitated cheating and anti-cheating tools are encountered in this industry. Popular platforms offer “provably fair gambling”, which guarantees that game results are not predetermined, thereby preventing manipulation.
However, some users have found a way to exploit these predetermined results by predicting patterns. The renowned iGaming platforms have responded with wallet behavior analysis, betting velocity checks, and transaction pattern analysis. These activities are supported by AI learning models.
Artificial intelligence is increasingly used in decentralized casino apps to detect duplicate accounts (enforcing a one-account-per-user policy), identity spoofing, and timing-based exploits.
For example, AI systems can detect multi-accounting, timing exploits, and even bot betting by tracking unique biometric-style data like reaction latency or device fingerprinting.
Opportunity for AI Companionship to Help
AI companions rely on mood detection, conversational memory, and behavior modeling to deliver personalized, emotionally consistent interactions, as well as some naughty sexting. Now, before you roll your eyes, do keep in mind that these techniques could potentially identify irregular user behavior, such as abrupt changes in tone or engagement, in other applications.
Although these models are not currently implemented as security tools, their architectural similarities to anti-cheat and fraud detection systems in gaming and social platforms highlight a significant overlap for future development.
For example, the ability to track subtle behavioral shifts could be adapted to flag suspicious activities in real time, leading to enhanced security in the online entertainment sector. This is another example of the versatility of AI, particularly in its monitoring capabilities.
Implications That Go Beyond Fair Play
It’s all about brothers turning against their own brothers (at least metaphorically) in terms of online cheating. As cheaters employ AI to create problems, gaming platforms rely on AI’s fast-learning capability to tackle these problems as soon as possible. The same goes for eSports, crypto games, or even emotionally intelligent avatars.
Humans are still part of the equation, but their role diminishes daily. That’s not necessarily a bad thing, especially if you’re on the side of the good guys, but let’s not forget that machines are getting smarter, and the implications of this shift are hard to predict.