Table game streamers exposed - fake money proof???

m@rCo

m@rCo

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ok so been watching lots of table game streams lately n noticed some REALLY sus stuff... gonna share what i found + clips to back it up. think most "big win" streamers using fake money/demo accounts...

red flags i noticed:
never show withdrawal process
same balance for hours despite losses
weird bet sizing that makes no sense
never run out of money despite -EV plays
casino names always hidden/blurred

collected evidence:
screenshots comparing real vs fake tables
video clips showing inconsistencies
deposit/withdrawal page differences
balance behavior patterns

not trying to start drama but think community should know truth... thoughts? anyone else noticed this stuff? got more evidence if interested but dont wanna post everything at once 🧐
 
excuse me?? 😤 i stream with real money and have ALL my withdrawals documented! dont put all streamers in same box! 🎰✨
 
Let's analyze this objectively. Red flags I've noticed:
  1. Identical bet patterns across sessions
  2. No transaction timestamps
  3. Balance inconsistencies during long sessions
  4. Suspicious "technical issues" during big cashouts
  5. Demo UI elements visible in reflection/overlays
 
some of us actually play legit tho! 💕 this witch hunt hurting real streamers fr...
 
Worth noting taht some casinos offer special deals to streamers. Doesn't make it right, but explains some patterns
 
m@rCo said:
@5.o.2 like you really can do this?
Click to expand...
something like this:
Python: 
import cv2
import numpy as np
from PIL import Image
import pytesseract

def detect_demo_watermark(frame):
    # Common demo mode indicators
    demo_templates = {
        'demo_text': cv2.imread('demo_template.png'),
        'practice_text': cv2.imread('practice_template.png'),
        'fun_play': cv2.imread('fun_play_template.png')
    }
    
    # Convert frame to grayscale for better matching
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Areas where demo watermarks typically appear
    roi_zones = [
        (0, 0, frame.shape[1]//3, frame.shape[0]//4),  # Top left
        (frame.shape[1]*2//3, 0, frame.shape[1], frame.shape[0]//4),  # Top right
        (0, frame.shape[0]*3//4, frame.shape[1], frame.shape[0])  # Bottom
    ]
    
    # Check each ROI for demo indicators
    for x1, y1, x2, y2 in roi_zones:
        roi = gray[y1:y2, x1:x2]
        
        # Template matching
        for template in demo_templates.values():
            result = cv2.matchTemplate(roi, template, cv2.TM_CCOEFF_NORMED)
            if np.max(result) > 0.8:  # Threshold for match confidence
                return True
                
        # OCR check for demo text
        text = pytesseract.image_to_string(roi)
        if any(keyword in text.lower() for keyword in ['demo', 'practice', 'fun play']):
            return True
    
    return False

def analyze_stream(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_count = 0
    demo_frames = 0
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
            
        if frame_count % 30 == 0:  # Check every 30 frames
            if detect_demo_watermark(frame):
                demo_frames += 1
                
        frame_count += 1
    
    cap.release()
    return demo_frames / frame_count  # Return percentage of frames with demo indicators

This script would:
  1. Load stream footage and process frame by frame
  2. Check specific regions where demo watermarks typically appear
  3. Use both template matching and OCR to detect demo indicators
  4. Track percentage of frames showing demo elements
  5. Could be expanded to include other detection methods
The actual implementation would need more refinement and error handling, but this gives a basic idea of the approach.
 
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