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Robocalypse Now: Using Deep Learning to Combat Cheating in CSGO [video]

84 points by doppp 9y ago

RenegadeEagle 9y ago
I haven't watched this talk yet, but as an avid CS:GO player, this isn't doing much to combat cheaters.
- NamTaf 9y ago
  This is a really amusing comment after watching the 9-12 minute segment of the video. :) For what it's worth, I very rarely run into cheaters. I couldn't specifically identify the last time I ran into one, in fact.
  - tehlike 9y ago
    It sounds like WAI.
  - efini 9y ago
    Those who cheat and don't get caught are very good at hiding it. When all it takes to toggle is a hotkey like NUMPAD1 to activate aimlock/walls and NUMPAD0 to turn all cheats off, it makes it almost impossible to identify a cheat.
dvt 9y ago
I built (and tried to get funding for) http://gameref.io/ http://gameref.io/ a few years ago. Many critics argued that a deep learning or AI solution would be better. For what it's worth, I also played Counter-Strike at a professional level from '04-'07 -- my CS:Source team was ranked #7 in North America in 2005 (according to GotFrag.com). After some research and a few POCs I am 100% certain a ML solution will not work in detecting cheats other than completely trivial "spinbots" or "aim-lock" aimbots. For example, it by definition cannot catch an entire class of cheats (wall hacks), but it even falters when trying to catch aimbots; the presentation here argues for a "heuristic-based" method, but, again, these methods often fail when (if you look at the source), aimbots are often coded to (a) move smoothly/naturally and (b) introduce random noise. In my case, I tried to at the very least combat one class of cheats with close to 100% confidence. These kinds of talks are great at conferences, but, as they say, the proof is in the pudding. Cheaters are very hard to catch, and cheat coders are much smarter than generalized AI methods.
- rudedogg 9y ago
  gameref looks like a really clever and simple solution as a hardware device. > After some research and a few POCs I am 100% certain a ML solution will not work in detecting cheats other than completely trivial "spinbots" or "aim-lock" aimbots. I'm curious why you say this. I'm a machine learning noob, but it seems pretty straightforward that it would work. Relating to wallhacks, they can use your last 1,000 matches, and if you're never/rarely surprised by a camping enemy, you're probably cheating. Your camera position moments before interaction with an obscured enemy is probably another indicator.
  - dvt 9y ago
    > I'm curious why you say this. Well the answer is surprisingly simple. Suppose you only have one person that's wallhacking on your team. He's calling out positions for everyone else. He can play terribly, but since the calls are going out, your team still wins. (This is not a hypothetical and has actually happened in high level matches before.)
    - jzwinck 9y ago
      How high level can a team get in CS (in terms of national or world rankings) without playing in a controlled environment where this sort of cheating would be more easily detected by local human observers? Could my team be ranked in the top ten in the US or Korea or wherever if we only ever play via the internet?
      - dvt 9y ago
        > How high level can a team get in CS (in terms of national or world rankings) without playing in a controlled environment where this sort of cheating would be more easily detected by local human observers? Very high. You can see for yourself here[1]. Anecdotally, I had a player on my team that cheated in an (online) semi-final match. We had no idea, but were moved down a league, were disgraced, and obviously cut him. A year later, he was one of the people that toured the country with the XFX team. I couldn't believe it. Every one knew this guy cheated just a few months prior, now he was offered a contract. I was very active in the Socal LAN scene, and know at least 3-4 players that played professionally and cheated. As far as your second point is concerned, some aimbots are so tricky, even local human observers couldn't catch them. I saw this proof of concept made by a famous cheat maker where the mouse would "tug" at the direcction of the player through a wall. The movie would be almost imperceptible by a spectator, but definitely "felt" by the player. [1] https://www.hltv.org/blog/13538/cheating-on-professional-level-of-csgo https://www.hltv.org/blog/13538/cheating-on-professional-lev...
sturadnidge 9y ago
I’d be curious to know if there was a difference in engagement from Overwatchers. I basically stopped doing cases because there was so much junk in queue (rage reporting, skilled players playing against unskilled players, aka ‘smurfing’, etc). But after the introduction of VACnet the case quality went through the roof, i no longer felt like i was wasting my time doing cases and thus do more of them now.
narrator 9y ago
I have long said that the future will be our AI bots vs their AI bots with the humans stuck in the middle of it all just trying to have a life.
Hydraulix989 9y ago
Now cheat by using a generative network to combat the deep learning that combats cheaters.
- CNJ7654 9y ago
  What a time to be alive

84 points by doppp 9y ago

RenegadeEagle 9y ago

I haven't watched this talk yet, but as an avid CS:GO player, this isn't doing much to combat cheaters.

NamTaf 9y ago
This is a really amusing comment after watching the 9-12 minute segment of the video. :) For what it's worth, I very rarely run into cheaters. I couldn't specifically identify the last time I ran into one, in fact.
- tehlike 9y ago
  It sounds like WAI.
- efini 9y ago
  Those who cheat and don't get caught are very good at hiding it. When all it takes to toggle is a hotkey like NUMPAD1 to activate aimlock/walls and NUMPAD0 to turn all cheats off, it makes it almost impossible to identify a cheat.

dvt 9y ago

I built (and tried to get funding for) http://gameref.io/ http://gameref.io/ a few years ago. Many critics argued that a deep learning or AI solution would be better. For what it's worth, I also played Counter-Strike at a professional level from '04-'07 -- my CS:Source team was ranked #7 in North America in 2005 (according to GotFrag.com). After some research and a few POCs I am 100% certain a ML solution will not work in detecting cheats other than completely trivial "spinbots" or "aim-lock" aimbots. For example, it by definition cannot catch an entire class of cheats (wall hacks), but it even falters when trying to catch aimbots; the presentation here argues for a "heuristic-based" method, but, again, these methods often fail when (if you look at the source), aimbots are often coded to (a) move smoothly/naturally and (b) introduce random noise. In my case, I tried to at the very least combat one class of cheats with close to 100% confidence. These kinds of talks are great at conferences, but, as they say, the proof is in the pudding. Cheaters are very hard to catch, and cheat coders are much smarter than generalized AI methods.

rudedogg 9y ago
gameref looks like a really clever and simple solution as a hardware device. > After some research and a few POCs I am 100% certain a ML solution will not work in detecting cheats other than completely trivial "spinbots" or "aim-lock" aimbots. I'm curious why you say this. I'm a machine learning noob, but it seems pretty straightforward that it would work. Relating to wallhacks, they can use your last 1,000 matches, and if you're never/rarely surprised by a camping enemy, you're probably cheating. Your camera position moments before interaction with an obscured enemy is probably another indicator.
- dvt 9y ago
  > I'm curious why you say this. Well the answer is surprisingly simple. Suppose you only have one person that's wallhacking on your team. He's calling out positions for everyone else. He can play terribly, but since the calls are going out, your team still wins. (This is not a hypothetical and has actually happened in high level matches before.)
  - jzwinck 9y ago
    How high level can a team get in CS (in terms of national or world rankings) without playing in a controlled environment where this sort of cheating would be more easily detected by local human observers? Could my team be ranked in the top ten in the US or Korea or wherever if we only ever play via the internet?
    - dvt 9y ago
      > How high level can a team get in CS (in terms of national or world rankings) without playing in a controlled environment where this sort of cheating would be more easily detected by local human observers? Very high. You can see for yourself here[1]. Anecdotally, I had a player on my team that cheated in an (online) semi-final match. We had no idea, but were moved down a league, were disgraced, and obviously cut him. A year later, he was one of the people that toured the country with the XFX team. I couldn't believe it. Every one knew this guy cheated just a few months prior, now he was offered a contract. I was very active in the Socal LAN scene, and know at least 3-4 players that played professionally and cheated. As far as your second point is concerned, some aimbots are so tricky, even local human observers couldn't catch them. I saw this proof of concept made by a famous cheat maker where the mouse would "tug" at the direcction of the player through a wall. The movie would be almost imperceptible by a spectator, but definitely "felt" by the player. [1] https://www.hltv.org/blog/13538/cheating-on-professional-level-of-csgo https://www.hltv.org/blog/13538/cheating-on-professional-lev...

sturadnidge 9y ago

I’d be curious to know if there was a difference in engagement from Overwatchers. I basically stopped doing cases because there was so much junk in queue (rage reporting, skilled players playing against unskilled players, aka ‘smurfing’, etc). But after the introduction of VACnet the case quality went through the roof, i no longer felt like i was wasting my time doing cases and thus do more of them now.

narrator 9y ago

I have long said that the future will be our AI bots vs their AI bots with the humans stuck in the middle of it all just trying to have a life.

Hydraulix989 9y ago

Now cheat by using a generative network to combat the deep learning that combats cheaters.

CNJ7654 9y ago
What a time to be alive