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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10848 |
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| _version_ | 1866918140523315200 |
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| author | Yoo, Jaeung Franciskus Kim, Huy Kang |
| author_facet | Yoo, Jaeung Franciskus Kim, Huy Kang |
| contents | Speedrun, a practice of completing a game as quickly as possible, has fostered vibrant communities driven by creativity, competition, and mastery of game mechanics and motor skills. However, this contest also attracts malicious actors as financial incentives come into play. As media and software manipulation techniques advance - such as spliced footage, modified game software and live stream with staged setups - forged speedruns have become increasingly difficult to detect. Volunteer-driven communities invest significant effort to verify submissions, yet the process remains slow, inconsistent, and reliant on informal expertise. In high-profile cases, fraudulent runs have gone undetected for years, allowing perpetrators to gain fame and financial benefits through monetised viewership, sponsorships, donations, and community bounties. To address this gap, we propose Tracer, Tamper Recognition via Analysis of Continuity and Events in game Runs, a modular framework for identifying artefacts of manipulation in speedrun submissions. Tracer provides structured guidelines across audiovisual, physical, and cyberspace dimensions, systematically documenting dispersed in-game knowledge and previously reported fraudulent cases to enhance verification efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracer: A Forensic Framework for Detecting Fraudulent Speedruns from Game Replays Yoo, Jaeung Franciskus Kim, Huy Kang Human-Computer Interaction Computers and Society Speedrun, a practice of completing a game as quickly as possible, has fostered vibrant communities driven by creativity, competition, and mastery of game mechanics and motor skills. However, this contest also attracts malicious actors as financial incentives come into play. As media and software manipulation techniques advance - such as spliced footage, modified game software and live stream with staged setups - forged speedruns have become increasingly difficult to detect. Volunteer-driven communities invest significant effort to verify submissions, yet the process remains slow, inconsistent, and reliant on informal expertise. In high-profile cases, fraudulent runs have gone undetected for years, allowing perpetrators to gain fame and financial benefits through monetised viewership, sponsorships, donations, and community bounties. To address this gap, we propose Tracer, Tamper Recognition via Analysis of Continuity and Events in game Runs, a modular framework for identifying artefacts of manipulation in speedrun submissions. Tracer provides structured guidelines across audiovisual, physical, and cyberspace dimensions, systematically documenting dispersed in-game knowledge and previously reported fraudulent cases to enhance verification efficiency. |
| title | Tracer: A Forensic Framework for Detecting Fraudulent Speedruns from Game Replays |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2509.10848 |