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Sometimes, as we’ve discussed in the past, we issue mitigations to minimize their impact privately to keep them in the game so we can absorb all the information we can about the account and the machine used to cheat. Often when a cheater appears in a match they are immediately kicked from the experience. This is one of the many ways Machine Learning helps identify and prioritize issues for our team, allowing Team Ricochet to develop new prevention strategies, detection techniques, and mitigations. A major focus for this and many advancements is Ranked Play modes across our titles, combating anyone attempting to jump the ranks of the leaderboard unfairly. We’re just getting started on Machine Learning integration for the Replay Investigation Tool, but we’re excited to see how it evolves over time. A single PC running the model can review up to 1,000 clips per day – a number that grows exponentially when multiple computers are tasked with operating this specific Replay Machine Learning Investigation model. Some clips are easy: the most egregious “rage hacking” is simple to spot, but the Replay Investigation Tool was helpful to identify hackers who used tools to give them a slight advantage that was harder to spot in-game, such as wall hacks.įor the launch of Modern Warfare III – and across all titles protected by RICOCHET: Anti-Cheat – the #TeamRICOCHET team is activating Machine Learning processes to increase the efficiency and strength of our anti-cheat efforts.įor the Replay Investigation Tool, a Machine Learning model is trained to identify suspicious behavior like wall hacks or raging (plus many others), and immediately prioritizes and alerts the team to review the issue for account action. On average, a #TeamRICOCHET teammate could review somewhere in the ballpark of 700 replay clips in any given day. This tool has been beneficial since it launched, but the team wanted to drive toward a new goal: Speed. Machine Learning x Replay Investigation ToolĮarlier this year we announced a replay investigation tool that captured gameplay data so it could be converted into video internally, allowing our teams to review player matches for problem behavior. One example of how we’re using Machine Learning to accelerate our anti-cheat capabilities is with the Replay tool. Machine Learning also helps enhance existing tools. Machine Learning works in concert with our team, providing information to make account decisions – but Machine Learning systems do not issue bans. In short, Machine Learning helps us anticipate behavior better and operate with more effectiveness, with our team validating for accuracy. Collecting and collating problem accounts for action.Issuing account challenges to validate abnormal behavior.Examining client and server data to find new cheat behaviors.Machine Learning advancements enhance our team’s ability by: Machine Learning advancements have been integrated into our tech to help with efficiency and speed in prevention, detection, and removal of cheaters. How Does #TeamRICOCHET Use Machine Learning? Machine Learning, in combination with client and server-side systems that continue to evolve and grow, helps advance both the speed and accuracy of our prevention techniques and detection systems. Combining everything #TeamRICOCHET has developed over the course of the last three years with new Machine Learning advancements, RICOCHET: Anti-Cheat™ is preparing for the launch of Call of Duty®: Modern Warfare® III with a stronger and faster process to combat cheating.