Channel: Basically Homeless

Category: Gaming

Tags: mr homelesswaldo open source visual anti cheatmachine learning anti cheatthe solution to machine learning ai cheats in fps gamescheating in warzonethe problem with the gaming industrydeclaration of war against hacking in fps gamescheaters in gamingthe streaming industryanti-cheathackers are winningfpsfps gamingesports industryricochet anticheatopen source anti cheatwarzone hackscheating in esports and lanhackusationsfps games are about to die

Description: The rise of "closet hacking" poses a massive threat to what is not only a passtime & means of community, but a quickly growing industry that is esports and streaming. This video outlines the issues with the industry today, and proposes a theoretical solution to one of the leading problems within the industry: Performance enhancing humanized machine assistance. JOIN TO HELP discord.com/invite/SgpnrUm github.com/waldo-vision waldo.vision TIME STAMPS: 00:00 What would you give up? The problem 01:26 Cheating in competition 03:09 Incentive "It's just a game" 08:26 The rise of closet cheating 11:37 Declaration of War - Phase 1 WALDO HOW CAN YOU HELP? See the github FAQ page for more information on how you can help github.com/jaredb1011/waldo-anticheat/discussions/4 Join the discord and join in the discussion. Creative ideas are needed just as much as developers. discord.com/invite/SgpnrUm What is WALDO? A deep learning Artificial Intelligence (A.I.) can detect the human behavioral characteristics of a user within a video game. We plan to train an A.I. to understand how humans play video games via a visual machine learning program. Once the program understands how humans play video games based on gameplay footage, we can then feed the it gameplay footage to determine if the player in the footage is receiving assistance from a 3rd party "hack" or "cheat" program. The first goal to acheive is detection of the most prevalent "closet hack" which is humanized aim-assist. We will build a program that can extrapolate mouse movement data from gameplay footage via 3D imaging and optical flow. Once accurate mouse data can be logged from gameplay footage successfully, we can then begin to train the A.I. what human aim looks like in a video game. This will be done by providing data to the program from gameplay that has been verified as human. With enough data, we will have a trained A.I. program that can tell if aim within any given piece of gameplay footage is human or machine assisted. Future iterations of the program will include detection of many more forms of closet hacking. Phase 1 focuses primarily on humanized aim-assist. Upon completion of phase 1, WALDO's main function will be vindication and clarity to many recent "hackusations." Some of the creators in the video: -------------------------------------------------------------------------------- youtube.com/c/LinusTechTips\ youtube.com/channel/UCFLFc8Lpbwt4jPtY1_Ai5yA LTTstore.com youtube.com/c/EsportsTalk youtube.com/c/DrDisRespect youtube.com/c/Nadeshot youtube.com/c/NickMercs youtube.com/user/timthetatman youtube.com/c/shroud youtube.com/user/summit1g -------------------------------------------------------------------------------- Music Attributions Music by Kevin MacLeod is licensed under a Creative Commons Attribution 4.0 license. creativecommons.org/licenses/by/4.0 Source: incompetech.com/music/royalty-free/index.html?isrc=USUAN1100731 Artist: incompetech.com -------------------------------------------------------------------------------- References Chen K-T, Hong L-W (2007) User identification based on game-play activity patterns. In: Proceedings of the 6th ACM SIGCOMM workshop on network and system support for games, pp 7–12. ACM Ahmad MA, Keegan B, Srivastava J, Williams D, Contractor N (2009) Mining for gold farmers: automatic detection of deviant players in MMOGs. In: International conference on computational science and engineering, 2009. CSE’09, vol 4, pp 340–345. IEEE Chung Y, Park C-Y, Kim N-R, Cho H, Yoon T, Lee H, Lee J-H (2013) Game bot detection approach based on behavior analysis and consideration of various play styles. ETRI J 35(6):1058–1067 Itsuki H, Takeuchi A, Fujita A, Matsubara H (2010) Exploiting MMORPG log data toward efficient rmt player detection. In: Proceedings of the 7th international conference on advances in computer entertainment technology, pp 118–119. ACM Kang AR, Kim HK, Woo J (2012) Chatting pattern based game bot detection: do they talk like us? TIIS 6(11):2866–2879 Mitterhofer S, Kruegel C, Kirda E, Platzer C (2009) Server-side bot detection in massively multiplayer online games. IEEE Secur Priv 3:29–36 Pao H-K, Chen K-T, Chang H-C (2010) Game bot detection via avatar trajectory analysis. IEEE Trans Comput Intell AI Games 2(3):162–175 Thawonmas R, Kurashige M, Chen K-T (2007) Detection of landmarks for clustering of online-game players. IJVR 6(3):11–16 --------------------------------------------------------------------------------