Merge pull request #11 from jaredb1011/Mr-Homeless-patch-1

readme changes

Author: jaredb1011

README.md

@@ -1,12 +1,27 @@
-# visual-anticheat
-A project that uses optical flow and machine learning to detect aimhacking in video clips from the game Apex Legends.
+# waldo-anticheat
+A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. 
+Check out this [video](https://youtu.be/GOI9EkLsUm0) discussing the purpose and vision of WALDO.
+
+# Notes
+* This project is still under development. 
 
 ## The What
-A new form of cheating in video games is using deep learning to implement an aim assist cheat.
-This form of cheating is very hard to detect because no game hacking or modification is necessary, so anti-cheats can't detect it easily.
+A new market for cheats that are visually indistinguishable to the human eye have lead to a rise in "closet hacking" among streamers and professionals.
+This form of cheating is extremely hard to detect. In some cases it issible to detect, even with today's most advanced anti-cheat software. 
 
-We're trying to combat this new kind of cheating by creating our own deep learning program to detect this behavior in video clips.
-We're starting with only one game, Apex Legends, to focus our efforts.
+We will combat this new kind of cheating by creating our own deep learning program to detect this behavior in video clips.
 
 ## The How
-Because deep learning aimhacks don't actually modify the game or computer in any way, the only way to detect this form of cheating is by observing the cheating behavior while spectating, or by analyzing the aiming data of a player. We don't have access to raw aim data for players, so our goal is to analyze the video directly using deep learning to detect aimhacking.
+Because of the advanced technology used, the only reliable way to detect this form of cheating is by observing the cheating behavior directly from the end result- gameplay. Our goal is to analyze the video directly using deep learning to detect if a user is receiving machine assistance.
+
+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."
+
+# Skills needed: 
+1. Machine learning / neural networks / AI 
+2. Visualizations and graphics 
+3. Data analysis 
+4. General python 
+5. Website design / programming 
+6. Game graphics / video analysis 
+7. Gamers
+8. Current closet hackers you can help ( ͡° ͜ʖ ͡°)