Experience error-free AI audio transcription that's faster and cheaper than human transcription. (Get started for free)
Aimbot cheats are some of the most frustrating for players to deal with in online shooters. Aimbots automatically lock onto targets' heads and allow cheaters to rack up kills with inhuman accuracy. However, developers are wise to these sneaky programs and have implemented systems to catch aimbot users red-handed.
One popular approach is analyzing players' statistics over time. While everyone has the occasional lucky match, aimbots create unnaturally consistent headshot rates. Developers can set acceptable thresholds for headshot percentages based on the player base. When someone exceeds that threshold match after match, it triggers an investigation.
Some games go further and actively analyze each shot as it happens. They look at factors like how quickly the player swivels and locks onto the head of an opponent who just came into view. The speed and precision of those movements can indicate the use of an aimbot.
Developers also leverage community reports. If multiple players report someone for aimbotting after a match, it prompts a review. Experienced players familiar with a game's mechanics often can tell when something seems off about a player's movement and accuracy. Their reports provide invaluable crowdsourced evidence.
Companies like Valve have created complex, ever-evolving systems for identifying cheaters in popular shooters like Counter-Strike. Valve states that they utilize "deep learning and neural networks" to detect aimbot usage. This advanced AI analyzes massive datasets from players to learn the differences between legitimately skilled players and cheaters.
Wallhacks grant players the ability to see through walls and track opponent positions and movements. This provides an enormous unfair advantage, essentially ruining matches for legitimate competitors. Thankfully, developers have waged war on wallhacks and are implementing countermeasures to expose users.
One method is monitoring statistics like a player's pre-fire rate, which is the frequency of firing right as they round a corner or before an opponent is visible. Wallhack users pre-fire at opponents they can see through walls, resulting in abnormally high pre-fire rates. Developers look for dramatic spikes in pre-fire percentages to identify potential hackers.
Some games also analyze the locations where a player aims and fires. Wallhack users tend to constantly aim at opponents through walls when they think no one will notice. The game logs these target points and checks for patterns of aiming at enemies the player shouldn't be able to see. Clustering of aim points on obscured opponents raises red flags.
Developers might also seed cheat users into matchmaking pools and intentionally feed them positional data about opponents. If a suspected hacker pre-fires at these hidden test players, it's evidence they are illicitly seeing through walls. Devs leverage thousands of these honeypot accounts to reliably identify cheaters.
Hardware and software solutions outside the game also combat wallhacks. Kernel-level anti-cheat software blocks the memory manipulation wallhacks rely on. Some games require players use specific supervised hardware like gaming cafes. This restricts their ability to install hacks in the first place.
Ultimately, no single technique is foolproof. Successfully detecting wallhacks requires combining cutting-edge analytics with community vigilance. Developers like Riot Games leverage massive datasets and replays from observed matches to train advanced machine learning algorithms. These AIs identify subtle signs of cheating even veteran players might miss.
Spinbotting allows cheaters to rapidly spin their characters in circles during gameplay, making it nearly impossible for opponents to land shots on them. This grants an absurd advantage, as spinbot users effortlessly mow down dizzy foes. Thankfully, developers are now catching on to this trickery by deploying special software to identify spinbotters.
The key is analyzing player movement for unnatural patterns. Most players move their crosshairs fairly smoothly as they track targets and aim. But spinbotters whirl around at insane speeds no human could match. Developers look for dramatic spikes in a player's rotations per minute as a red flag. If they spin faster than what is humanly possible on a regular basis, it's a strong indicator of cheating via spinbots.
Some games also model players' typical movement patterns during matches using machine learning algorithms. They analyze massive datasets of player behavior to create profiles of normal movement. When a player deviates from their established patterns by suddenly spinning like a Beyblade, it raises alarms. The software detects movement that a legitimate player is statistically unlikely to exhibit.
In addition, developers monitor players from match to match. While one freakishly fast spin session could be excused, repeated instances across multiple matches provide definitive evidence of spinbot use. Some anti-cheat systems also leverage community reporting, taking heed when multiple users submit complaints about a spinbotter after a game.
Ultimately, spinbots produce movement that is simply unnatural. Their superhuman spinning and precise aim while gyrating rapidly contrasts starkly with organic player behavior. Leveraging big data analytics and community feedback allows developers to reliably detect their use. This preserves competitive integrity and prevents these dizzying hacks from ruining the fun.
Game studios are also exploring new techniques like using VR headsets to analyze players' real-world movements during matches. Spinbottting requires no actual turning motion. If a player's character spins rapidly without corresponding head movement, it's a telltale sign of cheating. These innovative systems ensure that spinbottters have nowhere left to hide.
Some genres like first-person shooters are particularly plagued by spinbotting. However, no game is immune. Developers across mobile, console and PC platforms are taking action by deploying intelligent software to identify cheaters. Many incorporate anti-spinbot measures directly into their games. Companies like Valve and Riot also offer advanced anti-cheat packages other studios can integrate into their titles.
Maphacking allows cheaters to see the locations of opponents, objectives, and resources on game maps in real time. This provides an enormous advantage in strategy games and MOBAs. Thankfully, studios are leveraging machine learning to identify suspicious map vision patterns and bust maphack users.
Maphacks work by illicitly exposing data about the game map that should be hidden at certain times. For example, they reveal the locations of enemy players obscured by fog of war in real-time strategy games. Machine learning systems can model players' typical fog of war vision through massive datasets. When a player sees things they shouldn't be able to, such as opponents behind fog of war, it triggers alerts.
Studios gather vast datasets of normal vision patterns from tens of thousands of matches. Advanced neural networks process this data to learn legitimate fog of war limitations for each map area over time. When a player demonstrates repeated vision beyond what is feasible, the machine learning system flags them for investigation.
Riot Games utilizes these techniques in League of Legends to combat maphacking. Their algorithms analyze player vision heatmaps, looking for hotspots where a player concentrates vision suspiciously beyond fog of war. Riot also confirmed that machine learning helps identify maphack users who attempt to "mask" their cheating by pretending not to see opponents they actually observe via hacks. The algorithms still detect subtly unnatural vision patterns these cheaters exhibit.
Blizzard Entertainment employs similar machine learning systems in their games. StarCraft II's anti-cheat AI analyzes fog of war vision data from thousands of matches. It learns expectations for legitimate vision limitations on each map. When players see things improbably fast, the AI recognizes their map vision differs from normal patterns. This underscores machine learning's potential to expose maphack cheaters who think they've found sneaky ways to avoid detection.
Camping, or staying in one area of the map for an extended period, can be an extremely frustrating tactic to deal with in multiplayer shooters. It allows players to catch opponents off guard and rack up easy kills. Some campers even exploit glitches to reach seemingly inaccessible areas and snipe with impunity. Thankfully, developers are getting better at leveraging footstep tracking to identify serial campers and force them to keep moving.
In many shooters, footsteps create sound cues that players can use to monitor opponents" movements and locations. Campers try to minimize footsteps to conceal their presence. However, developers are hip to this trick. Sophisticated tracking systems in games like Call of Duty log the number of footsteps and footsteps sounds players generate. Those who stay nearly silent match after match stick out. The lack of footstep data indicates they are camping instead of actively maneuvering.
Games also analyze the areas where players generate footsteps. Campers tend to be extremely stationary, producing footstep sounds only within a small radius for long periods. On the other hand, moving players generate footsteps across wider areas as they travel the map. Machine learning algorithms examine footprint heatmaps to distinguish campers confined to tiny zones from normal players covering ground.
Some games even record specific materials footsteps interact with, like metal or grass. If a player"s footsteps only sound like concrete for 10 straight minutes, it"s a red flag they are standing still in a building. Advanced systems can integrate all this footstep data to assess if a player is staying put suspiciously long. Developers want camping kept within reason, so leveraging footstep tracking helps identify and address extreme cases.
Veteran players also rely on footstep cues themselves to root out campers. Keen listeners can follow sounds of footsteps disappearing into buildings to zero in on campers" locations. Or they can bait out clueless campers by making fake footsteps with gear. The arms race around footsteps underscores their importance as key data points. Developers leverage that data to keep excessive camping in check and ensure matches stay exciting.
Headshot percentage, or the rate at which a player scores headshots out of their total shots fired, is one of the most telling stats monitors by developers looking to identify potential cheaters. While skilled players may maintain headshot rates of 30% or more, averages tend to fall between 5-15% in most popular online shooters. When a player's headshot rate far exceeds the norm match after match, it raises immediate red flags.
Respawn Entertainment, developer of Apex Legends, has cited headshot percentage as one of the most important factors in their cheating detection algorithms. They closely monitor players with headshot rates exceeding 80%, which exceeds even the best human players. Continuous headshot rates above 50% also warrant investigation according to Respawn. Many proven cheaters eventually banned by Respawn demonstrate obvious patterns like maintaining 70%+ headshot rates, firing off high headshot streaks of 20 or more, and recording improbably high damage totals.
Riot Games also keys in on headshot percentages in Valorant. They have stated that one of the clearest signs of an aimbot or triggerbot hack is a headshot percentage of over 50%. Encountering players with rates in the 80-100% range leaves no doubt that cheating is involved. Riot's anti-cheat lead Paul Chamberlain dissected one match where a spinbotter maintained a 96% headshot percentage while spinning nonstop. Just 4% of their shots went elsewhere. He noted that amateur hackers often give themselves away quickly via these insanely high headshot rates.
Treyarch, developer of Call of Duty: Black Ops Cold War, has noted similar trends. Addressing widespread cheating concerns in Warzone, Treyarch stated that headshot percentages around or above 50% are reliably indicative of aimbots. They reminded players that the top human headshot rates only reach ~30% at best. Skilled human players miss headshots frequently; aimbots do not. Treyarch concurred with Respawn that headshot rates in the 80-100% range confirm cheating beyond any doubt.
One of the most disruptive cheats players can encounter in multiplayer games is damage modification. Damage mods allow cheaters to manipulate the damage they deal and receive, heavily tipping the scales in their favor during firefights. A player using a damage hack may be able to kill opponents with just one or two shots or soak up absurd amounts of damage before going down. These hacks completely undermine the careful balance developers establish for weapons and combat. Thankfully, studios are getting better at detecting their use through analytics and community reporting tools.
Damage mods create clear statistical anomalies that stand out when monitoring player data. For example, a damage hacker may be dealing 2-3 times the damage per shot compared to baseline expectations for the weapons they use. Developers establish damage profiles defining normal ranges for each gun. When a player regularly exceeds these boundaries, it prompts investigation. Damage mods also allow players to absorb abnormal amounts of damage before dying. Developers look for situations where a player improbably survives multiple direct shots from high-powered weapons. This indicates potential damage reduction or invincibility hacks.
The experiences of players encountering damage cheaters further demonstrate how these hacks destroy gameplay integrity. A Reddit user described being killed instantly from full health by a single body shot from an opponent's pistol. The player tested the same pistol themselves: even a headshot could not one-hit kill full health enemies. This huge damage differential exposed their opponent's damage mod. Another poster shared a video where an opponent survived a direct cruise missile strike along with 10+ bullets. The modder then instakilled the player from full health with a partial burst from an SMG. Others cited seeing modders shrug off multiple sniper headshots without dying. These accounts illustrate how blatantly damage hacks undermine legitimate gameplay.
One of the clearest indicators that a player is using an aimbot or other accuracy hack is their ability to reliably hit shots that are simply impossible for an unaided human. These can include headshots around walls, landing every bullet in a full auto spray at extreme ranges, and instantly flicking between targets' heads. Games like Valorant leverage AI systems specifically trained to recognize when a shot or sequence of shots exceeds human limitations.
Riot Games developed an AI technique called Vigilance for Valorant to catch cheaters exploiting accuracy hacks. As players take shots in matches, Vigilance analyzes the pre-shot positioning of the target and shooter frame-by-frame. It estimates the probability a human player would be able to make the required physical motions to land that shot. Factors include distance, target visibility, recoil, and reaction time needed. When a shot has an infinitesimal chance of being made organically, but the player nails it anyway, alarms sound.
Other AIs assess shooting accuracy across longer time periods and wider datasets. Activision patented a system that examines shooter accuracy, reaction time, and precision on a per match and aggregate basis. It builds a historical profile of a player's typical capabilities. When their performance exceeds personal norms by improbable margins, such as newfound ability to insta-flick headshot distant strafing targets, the AI deems cheating likely.
Blizzard's Overwatch leverages Deep Learning AI to find geometric patterns in the aim and actions of cheaters that humans struggle to perceive. By analyzing massive datasets, it learns how aimbot users move and target opponents on a sub-pixel level. It spots subtle unnatural tendencies legitimate players don't exhibit, like machine-perfect recoil control. Capcom also trained neural networks on pixel-perfect input sequences from aimbots. It can now recognize inhuman plays the moment they occur in live matches.
YouTube channel Two Epic Buddies demonstrated Valorant's Vigilance first-hand. When one used a basic aimbot, his otherwise human-looking movement immediately triggered Vigilance. An AI warning popped up on his screen about "improbable shot accuracy." Even though the hacker tried playing tactically, the shots themselves gave him away. This illustrates AIs' potential to detect even close-range aiming that might seem reasonable to humans, but statistically exceeds innate human capabilities.
Bunny hopping and strafing are movement techniques that allow players to make their characters harder to hit during matches. Bunny hopping involves rapidly jumping and air strafing in sync to maintain speed and momentum. Meanwhile, strafing refers to erratic side-to-side movement patterns as players engage opponents. When executed skillfully, these maneuvers significantly increase a player"s survivability in combat. However, some players misuse bunny hopping and strafing mechanics by automating them via movement hacks. These cheaters bunny hop perfectly or strafe at inhuman speeds, ruining gameplay for others. Thankfully, developers are getting better at detecting unnatural movement patterns to bust illicit bunny hoppers and strafers.
The key indicators of cheating bunny hoppers and strafers are the superhuman speeds and precision they achieve. According to researchers, the fastest possible human bunny hopping still limits players to around 80% of normal running speed due to physical limitations. Yet hacking bunny hoppers can maintain 100% speed or more while hopping nonstop. Games can analyze movement speeds during matches to identify anomalies exceeding human capabilities. For example, if a player maintains sprint speed while continuously bunny hopping, it signals automation. Human stamina restrictions make that unrealistic. Some companies like Activision have patented systems to model each player"s physical limits based on their individual play history. When a player suddenly surpasses their established norms, it raises red flags.
In terms of strafing, the biggest giveaway is the angular velocity achieved by cheaters. One expert analysis showed that even champion gamers rarely exceed 500 degrees/second during organic strafing. Yet a strafe hack allows speeds exceeding 1000 deg/s " double the human peak. Developers can monitor players" angular velocity in real-time for dramatic spikes indicative of strafe cheating. Another red flag is the perfect rhythm many strafe hackers maintain while firing accurately, eliminating the normal pauses human players exhibit to reacquire targets. Analytics of recoil patterns, rhythm, and aim can reveal the mechanical nature of enhanced strafing.