Sucessful discrimination; updated README

README.md

@@ -5,3 +5,4 @@ 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.  
+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.  

discriminator.py

@@ -60,8 +60,6 @@ def train(model, seq_len=16*64):
         ltLoss = ltLoss*0.9 + 0.1*loss.item()
         lltLoss = lltLoss*0.9 + 0.1*ltLoss
         print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss, 'correct?': correct })
-        if ltLoss < 0.20 and lltLoss < 0.225:
-            print("[*] Hell Yeah! Poccing! Got sup")
         if epoch % 8 == 0:
             torch.save(model.state_dict(), 'model_savepoints/'+tid+'_'+str(epoch)+'.n')