bs-constant now only in one place and updated README

README.md

@@ -6,3 +6,5 @@ I made it because I want to try to break it.
 This will work iff I succeed in building a PPT-discriminator for sha256 from randomness  
 As my first approach this discriminator will be based on an LSTM-network.  
 Update: This worked out way better than expected; given long enought sequences (128 Bytes are more than enough) we can discriminate successfully in 100% of cases.  
+Update 2: I did an upsie in the training-code and the discriminator is actually shit.  
+Update 3: Turns out: sha256 produces fairly high quality randomness and this project seems to have failed...  

discriminate.py

@@ -8,8 +8,9 @@ import numpy as np
 import random
 
 import shark
+from model import Model
 
-bs = int(256/8)
+bs = shark.bs
 
 class Model(nn.Module):
     def __init__(self):

model.py

@@ -0,0 +1,28 @@
+import torch
+from torch import nn
+from torch import nn, optim
+from torch.utils.data import DataLoader
+
+import shark
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.lstm = nn.LSTM(
+            input_size=8,
+            hidden_size=16,
+            num_layers=5,
+            dropout=0.01,
+        )
+        self.fc = nn.Linear(16, 1)
+        self.out = nn.Sigmoid()
+
+    def forward(self, x, prev_state):
+        output, state = self.lstm(x, prev_state)
+        logits = self.fc(output)
+        val = self.out(logits)
+        return val, state
+
+    def init_state(self, sequence_length):
+        return (torch.zeros(5, 1, 16),
+                torch.zeros(5, 1, 16))

shark.py

@@ -3,12 +3,6 @@ import math
 import os
 import random
 
-# Shark is a sha256+xor based encryption.
-# I made it because I want to try to break it.
-# (Precisely: Show it does not provide semantic security, because it is not IND-CPA-secure)
-# This will work iff I succeed in building a PPT-discriminator for sha256 from randomness
-# As my first approach this discriminator will be based on an LSTM-network.
-
 bs = int(256/8)
 
 def xor(ta,tb):

train.py

@@ -4,37 +4,16 @@ from torch import nn, optim
 from torch.utils.data import DataLoader
 import numpy as np
 import random
+import math
 
 import shark
+from model import Model
 
-class Model(nn.Module):
+def train(model, seq_len=16*256): # 0.5KiB
-    def __init__(self):
-        super(Model, self).__init__()
-        self.lstm = nn.LSTM(
-            input_size=8,
-            hidden_size=16,
-            num_layers=3,
-            dropout=0.1,
-        )
-        self.fc = nn.Linear(16, 1)
-        self.out = nn.Sigmoid()
-
-    def forward(self, x, prev_state):
-        output, state = self.lstm(x, prev_state)
-        logits = self.fc(output)
-        val = self.out(logits)
-        #print(str(logits.item())+" > "+str(val.item()))
-        return val, state
-
-    def init_state(self, sequence_length):
-        return (torch.zeros(3, 1, 16),
-                torch.zeros(3, 1, 16))
-
-def train(model, seq_len=16*64):
     tid = str(int(random.random()*99999)).zfill(5)
     print("[i] I am "+str(tid))
-    ltLoss = 50
+    ltLoss = 0.75
-    lltLoss = 51
+    lltLoss = 0.80
     model.train()
 
     criterion = nn.BCELoss()
@@ -44,13 +23,15 @@ def train(model, seq_len=16*64):
     state_c = [None,None]
     blob = [None,None]
     correct = [None,None]
+    err = [None,None]
+    ltErr = 0.5
 
     for epoch in range(1024):
         state_h[0], state_c[0] = model.init_state(seq_len)
         state_h[1], state_c[1] = model.init_state(seq_len)
 
-        blob[0], _ = shark.getSample(min(seq_len, 16*(epoch+1)), 0)
+        blob[0], _ = shark.getSample(seq_len, 0)
-        blob[1], _ = shark.getSample(min(seq_len, 16*(epoch+1)), 1)
+        blob[1], _ = shark.getSample(seq_len, 1)
         optimizer.zero_grad()
         for i in range(len(blob[0])):
             for t in range(2):
@@ -65,11 +46,16 @@ def train(model, seq_len=16*64):
                 optimizer.step()
 
                 correct[t] = round(y_pred.item()) == t
+                err[t] = abs(t - y_pred.item())
             ltLoss = ltLoss*0.9 + 0.1*loss.item()
-            lltLoss = lltLoss*0.9 + 0.1*ltLoss
+            ltErr = ltErr*0.99 + (err[0] + err[1])*0.005
-        print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss, 'ok0': correct[0], 'ok1': correct[1], 'succ': correct[0] and correct[1] })
+        lltLoss = lltLoss*0.9 + 0.1*ltLoss
-        if epoch % 8 == 0:
+        print({ 'epoch': epoch, 'loss': loss.item(), 'lltLoss': lltLoss, 'ok0': correct[0], 'ok1': correct[1], 'succ': correct[0] and correct[1], 'acc': str(int(100-(err[0]+err[1])*50))+"%" })
-            torch.save(model.state_dict(), 'model_savepoints/'+tid+'_'+str(epoch)+'.n')
+        torch.save(model.state_dict(), 'model_savepoints/'+tid+'_'+str(epoch)+'.n')
+    if 0.45 < ltErr < 0.55:
+        print("[~] My emperor! I've failed! A BARREL ROLL!")
+    else:
+        print("[~] Booyaaa!!!!")
 
 model = Model()