alexandremuzio / deep-steg Goto Github PK
View Code? Open in Web Editor NEWGlobal NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"
License: MIT License
Global NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"
License: MIT License
I would like to ask that at the end of the paper, "The proposed embeddings described in this paper are not intended for use with lossy image files", but here uses the jpeg image in tiny imagenet. Why choose this data set?
the script used to run correctly, when i checked again lately i got this error
TypeError: 'list' object is not callable
on line
ae_loss.append(autoencoder_model.train_on_batch(x=[batch_S, batch_C],
y=np.concatenate((batch_S, batch_C),axis=3)))
in training section
NB_EPOCHS = 1000
BATCH_SIZE = 32
m = input_S.shape[0]
loss_history = []
for epoch in range(NB_EPOCHS):
np.random.shuffle(input_S)
np.random.shuffle(input_C)
t = tqdm(range(0, input_S.shape[0], BATCH_SIZE),mininterval=0)
ae_loss = []
rev_loss = []
for idx in t:
batch_S = input_S[idx:min(idx + BATCH_SIZE, m)]
batch_C = input_C[idx:min(idx + BATCH_SIZE, m)]
C_prime = encoder_model.predict([batch_S, batch_C])
ae_loss.append(autoencoder_model.train_on_batch(x=[batch_S, batch_C],
y=np.concatenate((batch_S, batch_C),axis=3)))
rev_loss.append(reveal_model.train_on_batch(x=C_prime,
y=batch_S))
# Update learning rate
K.set_value(autoencoder_model.optimizer.lr, lr_schedule(epoch))
K.set_value(reveal_model.optimizer.lr, lr_schedule(epoch))
t.set_description('Epoch {} | Batch: {:3} of {}. Loss AE {:10.2f} | Loss Rev {:10.2f}'.format(epoch + 1, idx, m, np.mean(ae_loss), np.mean(rev_loss)))
loss_history.append(np.mean(ae_loss))
im not sure what's wrong with it
Can the decoder be decoupled after training to be used independently (i.e. use the encoder and the decoder separately)?
There are only model files here, maybe share the model calling code?
Besides, when I open the log file, I find input_1, input_2 ... input_42. What are these inputs? What format of data should I feed into the model?
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