StepN may be relatively new to the metaverse world, but it always does “step up” on its game!
On the 3rd of June, StepN announced an update on the Beta version of their application.
The update addressed one of the main concerns of the developers and users- cheating.
This involves the cutting-edge AI model to stop cheating, namely, SMAC.
StepN is a navigation game in which the app records your activities and steps, much like your Fitbit.
STEPN, like every other NFT game, runs on a blockchain and has its own coin for in-game usefulness.
To begin jogging, you must first acquire a pair of virtual sneakers.
Each pair of sneakers is a two-dimensional Pokémon with unique attributes and rarity.
The statistics influence how far you can run before recharging.
They may also provide you with a speed bonus, allowing you to earn more GST while running.
What is Cheating?
Cheating is the intentional exploitation of game mechanics or loopholes in order to gain an unfair edge over others.
Furthermore, cheating has a bad meaning in any situation. And why shouldn’t it?
Cheaters in conventional video games deprive legitimate users of in-game resources, assets, or favorable gaming experiences.
It is widespread and annoying for those who play fairly.
Given the potential for cheating to disrupt well-designed game ecosystems, developers strive tirelessly to advance anti-cheating technologies.
Examples of Cheating in STEPN
- GPS Spoofing – attempting to fool the app by misrepresenting your position
- Motion Simulation – using gadgets designed to replicate the realistic movement
- Multimining – having access to StepN accounts from multiple devices at once.
People who cheat risk raising token and NFT prices unfairly, putting the overall structure out of sync.
Moreover, it is also clearly unethical. This is why combating cheating is a top concern at STEPN.
Introducing STEPN’S Anti-Cheating System — SMAC
For months, our developers have been examining the patterns around GPS tracking, motion sensor, and health data.
This was made possible with the help of machine learning and artificial intelligence.
The eventual result is an advanced M2E anti-cheating mechanism.
After 3 months of rigorous development, data analysis, and machine learning model training, we proudly present: SMAC – STEPN’s Anti-Cheating Model
How Does it Work?
Data is cross-referenced with our internal standard using a cutting-edge machine learning model.
In case of any problem, the user will be flagged internally.
The technology also recognizes third-party programs such as bots.
After their identification, they are to be disconnected.
What is An Autoencoders?
Autoencoders are essentially an uncontrolled learning approach comprised of neural networks composed of numerous layers of neurons.
Furthermore, autoencoders find low-dimensional approximations of high-dimensional data and then reconstruct the input.
Bottleneck — Contains the dataset’s truncated representation.
Decoder — Converts a low-dimensional dataset to a high-dimensional dataset.
Autoencoders are a very effective tool for not only identifying anomalies.
Rather it also helps in assisting in the identification of the factors that generated the abnormalities.
It is able to highlight anytime an anomaly arises due to its inability to precisely recreate the data to match the original.
Cheaters will not earn any awards throughout that session and will suffer endurance losses.
We will gradually expose additional punishments in future releases.
Also, note that Moonwalking is not a form of cheating!
However, the penalty may not immediately come into effect.
Because STEPN attempts to give early leniency, users may see immoral earning techniques continue to function.
The team prioritizes the game’s durability and stability, as seen by the emphasis on developing solid tokenomics.
Some may not consider these development major, however, it might be a priority to some for a better gaming experience.
We can’t wait to see what the future holds for this promising venture.
Do keep a lookout for any future updates!