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HIDER 2 REVEAL SOFTWARE
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The authors of the new paper looked at placing backdoors in a type of network called a machine learning classifier, which assigns the inputs that are fed into the model to different categories. Today’s leading machine learning models derive their power from deep neural networks - webs of artificial neurons arranged in multiple layers, with every neuron in each layer influencing those in the next layer. “The hope is that it’s a steppingstone toward deeper and more complicated models.” Beyond Heuristics “It was a very thought-provoking paper,” said Ankur Moitra, a machine learning researcher at the Massachusetts Institute of Technology. But the results establish a new theoretical link between cryptographic security and machine learning vulnerabilities, suggesting new directions for future research at the intersection of the two fields. The mathematical rigor of the new work comes with trade-offs, like a focus on relatively simple models.

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In a paper presented at last year’s Foundations of Computer Science conference, a team of computer scientists demonstrated how to plant undetectable backdoors whose invisibility is as certain as the security of state-of-the-art encryption methods. Researchers are now starting to analyze the security of machine learning models in a more rigorous way.

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But the approach has been largely trial and error, lacking formal mathematical analysis of how well those backdoors are hidden. To better understand such vulnerabilities, researchers have developed various tricks to hide their own sample backdoors in machine learning models.

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A company charged with developing a machine learning system for a client could insert a backdoor and then sell the secret activation key to the highest bidder.

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That makes it hard to understand how they might be manipulated.Ĭonsider, for instance, the software vulnerability known as a backdoor - an unobtrusive bit of code that can enable users with a secret key to obtain information or abilities they shouldn’t have access to. Yet even while image generators like DALL♾ 2 and language models like ChatGPT grab headlines, experts still don’t understand why they work so well. It also greatly reduces your chance of being third partied.Machine learning is having a moment. that's invaluable imo, especially if you're not the 'blindly rush in and win through sheer force' type of player, which im not. you'll see bullets whiz by or see bullet impacts on the walls/ground near you, but you still have no idea of the source of the bullets. idk if you've noticed, but gun audio for suppressed guns doesn't make a sound unless the source is very close or they're actively hitting you.

(minimap + sound) there aren't many other attachments that can claim the same level of efficiency.Īlso, it's not just about minimap suppression. 1/5 attachment slots for range, BV, recoil, and suppression. but imo it gets a lot more complex than that.įrom just a minmax standpoint, suppressors are incredibly slot efficient. You'll be on the UAV each sweep anyway no matter what The walking steadiness with high aiming stability is really nice and smooth + the flash hider gives the cleanest view when shooting + snappy ads speed + consistent recoil Eventually this worst case scenario is bound to happen but imo its rare enough that its a very fair trade offġ - the flash hider w/ only 'recoil stabilisation'Ģ - vertical foregrip w/ aim walking steadiness + recoil steadinessģ - Tac laser w/ ads speed + aiming stability If you have ghost perk you can hear the uav be called in and try to avoid shooting. If you have anything other than ghost perk you'll already be on the uav With the minimap changes in this game you can get away without a suppressor, the only situation where flash hiders will reveal you on the map is when the enemy has a uav active They have better ads times than most suppressors, way better recoil and visual recoil and they remove a lot of the barrel smoke that suppressors actually make worse

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