def next_batch(self):
'''
Draw the next batch from from the combined switchable queue.
'''
source, source_lengths, target, target_lengths = self._queue.dequeue_many(self._model_feeder.ph_batch_size)
sparse_labels = ctc_label_dense_to_sparse(target, target_lengths, self._model_feeder.ph_batch_size)
return source, source_lengths, sparse_labels
def setup_graph(self, input_audio_batch, target_phrase):
batch_size = input_audio_batch.shape[0]
weird = (input_audio_batch.shape[1] - 1) // 320
logits_arg2 = np.tile(weird, batch_size)
dense_arg1 = np.array(np.tile(target_phrase, (batch_size, 1)),
dtype=np.int32)
dense_arg2 = np.array(np.tile(target_phrase.shape[0], batch_size),
dtype=np.int32)
pass_in = np.clip(input_audio_batch, -2**15, 2**15 - 1)
seq_len = np.tile(weird, batch_size).astype(np.int32)
with tf.variable_scope('', reuse=tf.AUTO_REUSE):
inputs = tf.placeholder(tf.float32, shape=pass_in.shape, name='a')
len_batch = tf.placeholder(tf.float32, name='b')
arg2_logits = tf.placeholder(tf.int32,
shape=logits_arg2.shape,
name='c')
arg1_dense = tf.placeholder(tf.float32,
shape=dense_arg1.shape,
name='d')
arg2_dense = tf.placeholder(tf.int32,
shape=dense_arg2.shape,
name='e')
len_seq = tf.placeholder(tf.int32, shape=seq_len.shape, name='f')
logits = get_logits(inputs, arg2_logits)
target = ctc_label_dense_to_sparse(arg1_dense, arg2_dense,
len_batch)
ctcloss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits,
sequence_length=len_seq)
decoded, _ = tf.nn.ctc_greedy_decoder(logits,
arg2_logits,
merge_repeated=True)
sess = tf.Session()
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, "models/session_dump")
func1 = lambda a, b, c, d, e, f: sess.run(ctcloss,
feed_dict={
inputs: a,
len_batch: b,
arg2_logits: c,
arg1_dense: d,
arg2_dense: e,
len_seq: f
})
func2 = lambda a, b, c, d, e, f: sess.run(
[ctcloss, decoded],
feed_dict={
inputs: a,
len_batch: b,
arg2_logits: c,
arg1_dense: d,
arg2_dense: e,
len_seq: f
})
return (func1, func2)
def next_batch(self):
'''
Draw the next batch from from the combined switchable queue.
'''
source, source_lengths, target, target_lengths = self._queue.dequeue_many(self._model_feeder.ph_batch_size)
sparse_labels = ctc_label_dense_to_sparse(target, target_lengths, self._model_feeder.ph_batch_size)
return source, source_lengths, sparse_labels
def main(_):
initialize_globals()
if not FLAGS.test_files:
log_error('You need to specify what files to use for evaluation via '
'the --test_files flag.')
exit(1)
global alphabet
alphabet = Alphabet(FLAGS.alphabet_config_path)
scorer = Scorer(FLAGS.lm_weight, FLAGS.valid_word_count_weight,
FLAGS.lm_binary_path, FLAGS.lm_trie_path,
alphabet)
# sort examples by length, improves packing of batches and timesteps
test_data = preprocess(
FLAGS.test_files.split(','),
FLAGS.test_batch_size,
alphabet=alphabet,
numcep=N_FEATURES,
numcontext=N_CONTEXT,
hdf5_cache_path=FLAGS.hdf5_test_set).sort_values(
by="features_len",
ascending=False)
def create_windows(features):
num_strides = len(features) - (N_CONTEXT * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2*N_CONTEXT+1
features = np.lib.stride_tricks.as_strided(
features,
(num_strides, window_size, N_FEATURES),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
return features
# Create overlapping windows over the features
test_data['features'] = test_data['features'].apply(create_windows)
with tf.Session() as session:
inputs, outputs, layers = create_inference_graph(batch_size=FLAGS.test_batch_size, n_steps=-1)
# Transpose to batch major for decoder
transposed = tf.transpose(outputs['outputs'], [1, 0, 2])
labels_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size, None], name="labels")
label_lengths_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size], name="label_lengths")
sparse_labels = tf.cast(ctc_label_dense_to_sparse(labels_ph, label_lengths_ph, FLAGS.test_batch_size), tf.int32)
loss = tf.nn.ctc_loss(labels=sparse_labels,
inputs=layers['raw_logits'],
sequence_length=inputs['input_lengths'])
# Create a saver using variables from the above newly created graph
mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not checkpoint:
log_error('Checkpoint directory ({}) does not contain a valid checkpoint state.'.format(FLAGS.checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
logitses = []
losses = []
print('Computing acoustic model predictions...')
batch_count = len(test_data) // FLAGS.test_batch_size
bar = progressbar.ProgressBar(max_value=batch_count,
widget=progressbar.AdaptiveETA)
# First pass, compute losses and transposed logits for decoding
for batch in bar(split_data(test_data, FLAGS.test_batch_size)):
session.run(outputs['initialize_state'])
features = pad_to_dense(batch['features'].values)
features_len = batch['features_len'].values
labels = pad_to_dense(batch['transcript'].values)
label_lengths = batch['transcript_len'].values
logits, loss = session.run([transposed, loss], feed_dict={
inputs['input']: features,
inputs['input_lengths']: features_len,
labels_ph: labels,
label_lengths_ph: label_lengths
})
logitses.append(logits)
losses.extend(loss)
ground_truths = []
predictions = []
distances = []
print('Decoding predictions...')
bar = progressbar.ProgressBar(max_value=batch_count,
widget=progressbar.AdaptiveETA)
# Get number of accessible CPU cores for this process
num_processes = len(os.sched_getaffinity(0))
# Second pass, decode logits and compute WER and edit distance metrics
for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
seq_lengths = batch['features_len'].values.astype(np.int32)
decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width,
num_processes=num_processes, scorer=scorer)
ground_truths.extend(alphabet.decode(l) for l in batch['transcript'])
predictions.extend(d[0][1] for d in decoded)
distances.extend(levenshtein(a, b) for a, b in zip(labels, predictions))
wer, samples = calculate_report(ground_truths, predictions, distances, losses)
mean_edit_distance = np.mean(distances)
mean_loss = np.mean(losses)
# Take only the first report_count items
report_samples = itertools.islice(samples, FLAGS.report_count)
print('Test - WER: %f, loss: %f, mean edit distance: %f' %
(wer, mean_loss, mean_edit_distance))
print('-' * 80)
for sample in report_samples:
print('WER: %f, loss: %f, edit distance: %f' %
(sample.wer, sample.loss, sample.distance))
print(' - src: "%s"' % sample.src)
print(' - res: "%s"' % sample.res)
print('-' * 80)
if FLAGS.test_output_file:
json.dump(samples, open(FLAGS.test_output_file, 'w'), default=lambda x: float(x))
def next_batch(self):
source, source_lengths, target, target_lengths = self._example_queue.dequeue_many(
self._batch_size)
sparse_labels = ctc_label_dense_to_sparse(target, target_lengths,
self._batch_size)
return source, source_lengths, sparse_labels
def evaluate(test_data, inference_graph, alphabet):
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
FLAGS.lm_binary_path, FLAGS.lm_trie_path,
Config.alphabet)
def create_windows(features):
num_strides = len(features) - (Config.n_context * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2*Config.n_context+1
features = np.lib.stride_tricks.as_strided(
features,
(num_strides, window_size, Config.n_input),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
return features
# Create overlapping windows over the features
test_data['features'] = test_data['features'].apply(create_windows)
with tf.Session(config=Config.session_config) as session:
inputs, outputs, layers = inference_graph
# Transpose to batch major for decoder
transposed = tf.transpose(outputs['outputs'], [1, 0, 2])
labels_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size, None], name="labels")
label_lengths_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size], name="label_lengths")
sparse_labels = tf.cast(ctc_label_dense_to_sparse(labels_ph, label_lengths_ph, FLAGS.test_batch_size), tf.int32)
loss = tf.nn.ctc_loss(labels=sparse_labels,
inputs=layers['raw_logits'],
sequence_length=inputs['input_lengths'])
# Create a saver using variables from the above newly created graph
mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
if FLAGS.checkpoint_dir is not None:
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not checkpoint:
log_error('Checkpoint directory ({}) does not contain a valid checkpoint state.'.format(FLAGS.checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
logitses = []
losses = []
print('Computing acoustic model predictions...')
batch_count = len(test_data) // FLAGS.test_batch_size
bar = progressbar.ProgressBar(max_value=batch_count,
widget=progressbar.AdaptiveETA)
# First pass, compute losses and transposed logits for decoding
for batch in bar(split_data(test_data, FLAGS.test_batch_size)):
session.run(outputs['initialize_state'])
features = pad_to_dense(batch['features'].values)
features_len = batch['features_len'].values
labels = pad_to_dense(batch['transcript'].values)
label_lengths = batch['transcript_len'].values
logits, loss_ = session.run([transposed, loss], feed_dict={
inputs['input']: features,
inputs['input_lengths']: features_len,
labels_ph: labels,
label_lengths_ph: label_lengths
})
logitses.append(logits)
losses.extend(loss_)
ground_truths = []
predictions = []
print('Decoding predictions...')
bar = progressbar.ProgressBar(max_value=batch_count,
widget=progressbar.AdaptiveETA)
# Get number of accessible CPU cores for this process
try:
num_processes = cpu_count()
except:
num_processes = 1
# Second pass, decode logits and compute WER and edit distance metrics
for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
seq_lengths = batch['features_len'].values.astype(np.int32)
decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width,
num_processes=num_processes, scorer=scorer)
ground_truths.extend(alphabet.decode(l) for l in batch['transcript'])
predictions.extend(d[0][1] for d in decoded)
distances = [levenshtein(a, b) for a, b in zip(ground_truths, predictions)]
wer, samples = calculate_report(ground_truths, predictions, distances, losses)
mean_edit_distance = np.mean(distances)
mean_loss = np.mean(losses)
# Take only the first report_count items
report_samples = itertools.islice(samples, FLAGS.report_count)
print('Test - WER: %f, CER: %f, loss: %f' %
(wer, mean_edit_distance, mean_loss))
print('-' * 80)
for sample in report_samples:
print('WER: %f, CER: %f, loss: %f' %
(sample.wer, sample.distance, sample.loss))
print(' - src: "%s"' % sample.src)
print(' - res: "%s"' % sample.res)
print('-' * 80)
return samples
def __init__(self,
sess,
loss_fn,
phrase_length,
max_audio_len,
learning_rate=10,
num_iterations=1000,
batch_size=1):
"""
Set up the attack procedure.
Here we create the TF graph that we're going to use to
actually generate the adversarial examples.
"""
self.sess = sess
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.batch_size = batch_size
self.phrase_length = phrase_length
self.max_audio_len = max_audio_len
# Create all the variables necessary
# they are prefixed with qq_ just so that we know hich
# ones are ours so when we restore the session we don't
# clobber them.
self.delta = delta = tf.Variable(np.zeros((batch_size, max_audio_len),
dtype=np.float32),
name='qq_delta')
self.mask = mask = tf.Variable(np.zeros((batch_size, max_audio_len),
dtype=np.float32),
name='qq_mask')
self.cwmask = cwmask = tf.Variable(np.zeros(
(batch_size, phrase_length), dtype=np.float32),
name='qq_cwmask')
self.original = original = tf.Variable(np.zeros(
(batch_size, max_audio_len), dtype=np.float32),
name='qq_original')
self.lengths = lengths = tf.Variable(np.zeros(batch_size,
dtype=np.int32),
name='qq_lengths')
self.importance = tf.Variable(np.zeros((batch_size, phrase_length),
dtype=np.float32),
name='qq_importance')
self.target_phrase = tf.Variable(np.zeros((batch_size, phrase_length),
dtype=np.int32),
name='qq_phrase')
self.target_phrase_lengths = tf.Variable(np.zeros((batch_size),
dtype=np.int32),
name='qq_phrase_lengths')
self.rescale = tf.Variable(np.zeros((batch_size, 1), dtype=np.float32),
name='qq_phrase_lengths')
# Initially we bound the l_infty norm by 2000, increase this
# constant if it's not big enough of a distortion for your dataset.
self.apply_delta = tf.clip_by_value(delta, -2000, 2000) * self.rescale
# We set the new input to the model to be the abve delta
# plus a mask, which allows us to enforce that certain
# values remain constant 0 for length padding sequences.
self.new_input = new_input = self.apply_delta * mask + original
# We add a tiny bit of noise to help make sure that we can
# clip our values to 16-bit integers and not break things.
noise = tf.random_normal(new_input.shape, stddev=2)
pass_in = tf.clip_by_value(new_input + noise, -2**15, 2**15 - 1)
# Feed this final value to get the logits.
self.logits = logits = get_logits(pass_in, lengths)
# And finally restore the graph to make the classifier
# actually do something interesting.
saver = tf.train.Saver(
[x for x in tf.global_variables() if 'qq' not in x.name])
saver.restore(sess, "models/session_dump")
# Choose the loss function we want -- either CTC or CW
self.loss_fn = loss_fn
if loss_fn == "CTC":
target = ctc_label_dense_to_sparse(self.target_phrase,
self.target_phrase_lengths,
batch_size)
ctcloss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits,
sequence_length=lengths)
loss = tf.nn.relu(ctcloss)
self.expanded_loss = tf.constant(0)
elif loss_fn == "CW":
raise NotImplemented(
"The current version of this project does not include the CW loss function implementation."
)
else:
raise
# Set up the Adam optimizer to perform gradient descent for us
var_start = tf.global_variables()
self.train = tf.train.AdamOptimizer(learning_rate).minimize(
loss, var_list=[delta])
self.loss = loss
self.ctcloss = ctcloss
var_end = tf.global_variables()
new_vars = [
x for x in var_end if x.name not in [y.name for y in var_start]
]
sess.run(tf.variables_initializer(new_vars + [delta]))
# Decoder from the logits, to see how we're doing
self.decoded, _ = tf.nn.ctc_beam_search_decoder(logits,
lengths,
merge_repeated=False,
beam_width=1000)
def main(_):
initialize_globals()
if not FLAGS.test_files:
log_error('You need to specify what files to use for evaluation via '
'the --test_files flag.')
exit(1)
global alphabet
alphabet = Alphabet(os.path.abspath(FLAGS.alphabet_config_path))
# sort examples by length, improves packing of batches and timesteps
test_data = preprocess(FLAGS.test_files.split(','),
FLAGS.test_batch_size,
alphabet=alphabet,
numcep=N_FEATURES,
numcontext=N_CONTEXT,
hdf5_cache_path=FLAGS.hdf5_test_set).sort_values(
by="features_len", ascending=False)
def create_windows(features):
num_strides = len(features) - (N_CONTEXT * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2 * N_CONTEXT + 1
features = np.lib.stride_tricks.as_strided(
features, (num_strides, window_size, N_FEATURES),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
return features
test_data['features'] = test_data['features'].apply(create_windows)
with tf.Session() as session:
inputs, outputs = create_inference_graph(
batch_size=FLAGS.test_batch_size, n_steps=N_STEPS)
seq_lengths_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size])
decode_logits_ph = tf.placeholder(
tf.float32, [None, FLAGS.test_batch_size,
alphabet.size() + 1])
labels_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size, None])
label_lengths_ph = tf.placeholder(tf.int32, [FLAGS.test_batch_size])
decoded, _ = decode_with_lm(decode_logits_ph,
seq_lengths_ph,
merge_repeated=False,
beam_width=FLAGS.beam_width)
sparse_labels = tf.cast(
ctc_label_dense_to_sparse(labels_ph, label_lengths_ph,
FLAGS.test_batch_size), tf.int32)
loss = tf.nn.ctc_loss(labels=sparse_labels,
inputs=decode_logits_ph,
sequence_length=seq_lengths_ph)
distance = tf.edit_distance(tf.cast(decoded[0], tf.int32),
sparse_labels)
# Create a saver using variables from the above newly created graph
mapping = {
v.op.name: v
for v in tf.global_variables()
if not v.op.name.startswith('previous_state_')
}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not checkpoint:
log_error(
'Checkpoint directory ({}) does not contain a valid checkpoint state.'
.format(FLAGS.checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
logitses = []
batch_count = len(test_data) // FLAGS.test_batch_size
bar = progressbar.ProgressBar(max_value=batch_count - 1,
widget=progressbar.AdaptiveETA)
for batch in bar(split_data(test_data, FLAGS.test_batch_size)):
session.run(outputs['initialize_state'])
batch_features = pad_to_dense(batch['features'].values)
batch_features_len = batch['features_len'].values
full_step_len = np.full_like(batch_features_len, N_STEPS)
logits = np.empty([0, FLAGS.test_batch_size, alphabet.size() + 1])
for i in range(0, batch_features.shape[1], N_STEPS):
chunk_features = batch_features[:, i:i + N_STEPS, :, :]
chunk_features_len = np.minimum(batch_features_len,
full_step_len)
# pad with zeros if the chunk does not have enough steps
steps_in_chunk = chunk_features.shape[1]
if steps_in_chunk < FLAGS.n_steps:
chunk_features = np.pad(
chunk_features,
((0, 0), (0, FLAGS.n_steps - steps_in_chunk), (0, 0),
(0, 0)),
mode='constant',
constant_values=0)
output = session.run(outputs['outputs'],
feed_dict={
inputs['input']:
chunk_features,
inputs['input_lengths']:
chunk_features_len,
})
logits = np.concatenate((logits, output))
# we have processed N_STEPS so subtract from remaining steps
batch_features_len -= N_STEPS
# clip to zero
batch_features_len = np.maximum(
batch_features_len, np.zeros_like(batch_features_len))
logitses.append(logits)
ground_truths = []
predictions = []
distances = []
losses = []
bar = progressbar.ProgressBar(max_value=batch_count - 1,
widget=progressbar.AdaptiveETA)
for logits, batch in bar(
zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
seq_lengths = batch['features_len'].values
labels = pad_to_dense(batch['transcript'].values)
label_lengths = batch['transcript_len'].values
decoded_, loss_, distance_, sparse_labels_ = session.run(
[decoded, loss, distance, sparse_labels],
feed_dict={
decode_logits_ph: logits,
seq_lengths_ph: seq_lengths,
labels_ph: labels,
label_lengths_ph: label_lengths
})
ground_truths.extend(
sparse_tensor_value_to_texts(sparse_labels_, alphabet))
predictions.extend(
sparse_tensor_value_to_texts(decoded_[0], alphabet))
distances.extend(distance_)
losses.extend(loss_)
wer, samples = calculate_report(ground_truths, predictions, distances,
losses)
mean_edit_distance = np.mean(distances)
mean_loss = np.mean(losses)
# Filter out all items with WER=0 and take only the first report_count items
report_samples = itertools.islice((s for s in samples if s.wer > 0),
FLAGS.report_count)
print('Test - WER: %f, loss: %f, mean edit distance: %f' %
(wer, mean_loss, mean_edit_distance))
print('-' * 80)
for sample in report_samples:
print('WER: %f, loss: %f, mean edit distance: %f' %
(sample.wer, sample.loss, sample.distance))
print(' - src: "%s"' % sample.src)
print(' - res: "%s"' % sample.res)
print('-' * 80)
if FLAGS.test_output_file:
json.dump(samples,
open(FLAGS.test_output_file, 'w'),
default=lambda x: float(x))
class Attack:
def __init__(self, sess, loss_fn, phrase_length, max_audio_len, psdMaxes,
learning_rate=10, num_iterations=5000, window_size=2048,
step_per_window=4, batch_size=1, mp3=False,
onlyCTC=True, audio=None, psdShape=None):
"""
Set up the attack procedure.
Here we create the TF graph that we're going to use to
actually generate the adversarial examples.
"""
self.sess = sess
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.batch_size = batch_size
self.phrase_length = phrase_length
self.max_audio_len = max_audio_len
self.mp3 = mp3
self.psdMaxes = psdMaxes
self.window_size = window_size
self.step_per_window = step_per_window
# Create all the variables necessary
# they are prefixed with qq_ just so that we know which
# ones are ours so when we restore the session we don't
# clobber them.
frame_length = int(window_size)
frame_step = int(window_size//step_per_window)
fft_length = int(2**np.ceil(np.log2(frame_length)))
sample_rate = 16000
freq_res = sample_rate/window_size
time_res = frame_step/(sample_rate/1000)
sigma_time = 96. / time_res
sigma_freq = 15.625 / freq_res
self.regularizer = regularizer = tf.Variable(np.zeros((batch_size), dtype=np.float32), name='qq_regularizer')
self.psyTh = psyTh = tf.Variable(np.zeros((batch_size, psdShape[0], psdShape[1]), dtype=np.float32), name='qq_psyTh')
self.delta = delta = tf.Variable(np.zeros((batch_size, max_audio_len)).astype(np.float32)/2, name='qq_delta') name='qq_delta')
self.mask = mask = tf.Variable(np.zeros((batch_size, max_audio_len), dtype=np.float32), name='qq_mask')
self.original = original = tf.Variable(np.zeros((batch_size, max_audio_len), dtype=np.float32), name='qq_original')
self.lengths = lengths = tf.Variable(np.zeros(batch_size, dtype=np.int32), name='qq_lengths')
self.target_phrase = tf.Variable(np.zeros((batch_size, phrase_length), dtype=np.int32), name='qq_phrase')
self.target_phrase_lengths = tf.Variable(np.zeros((batch_size), dtype=np.int32), name='qq_phrase_lengths')
self.rescale = tf.Variable(np.zeros((batch_size,1), dtype=np.float32), name='qq_rescale')
# Initially we bound the l_infty norm by 2000, increase this
# constant if it's not big enough of a distortion for your dataset.
if(loss_fn == 'CTC'):
self.apply_delta = tf.clip_by_value(delta, -2000, 2000)*self.rescale
elif(loss_fn == 'CTCPSYCLIP'):
self.apply_delta = apply_delta = self.clipBatch(delta, psyTh, regularizer, psdMaxes, max_audio_len, window_size, step_per_window)
self.new_input = new_input = self.apply_delta*mask + original
#self.new_input = new_input = delta*mask + original
# We set the new input to the model to be the above delta
# plus a mask, which allows us to enforce that certain
# values remain constant 0 for length padding sequences.
if(loss_fn == 'CTC'):
self.new_input = new_input = self.apply_delta*mask + original
if(loss_fn == 'CTCPSYGRAD'):
self.new_input = new_input = self.delta*mask + original
# We add a tiny bit of noise to help make sure that we can
# clip our values to 16-bit integers and not break things.
if(loss_fn == 'CTC'):
noise = tf.random_normal(new_input.shape,
stddev=2)
pass_in = tf.clip_by_value(new_input+noise, -2**15, 2**15-1)
# Feed this final value to get the logits.
self.logits = logits = get_logits(new_input, lengths)
# And finally restore the graph to make the classifier
# actually do something interesting.
saver = tf.train.Saver([x for x in tf.global_variables() if 'qq' not in x.name])
saver.restore(sess, "models/session_dump")
self.loss_fn = loss_fn
if loss_fn == "CTC":
target = ctc_label_dense_to_sparse(self.target_phrase, self.target_phrase_lengths, batch_size)
ctcLoss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits, sequence_length=lengths)
# Slight hack: an infinite l2 penalty means that we don't penalize l2 distortion
# The code runs faster at a slight cost of distortion, and also leaves one less
# paramaeter that requires tuning.
if not onlyCTC:
loss = tf.reduce_mean((self.new_input-self.original)**2,axis=1)/regularizer + ctcLoss
else:
loss = ctcLoss
self.expanded_loss = tf.constant(0)
if loss_fn == "CTC":
target = ctc_label_dense_to_sparse(self.target_phrase, self.target_phrase_lengths, batch_size)
ctcLoss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits, sequence_length=lengths)
# Slight hack: an infinite l2 penalty means that we don't penalize l2 distortion
# The code runs faster at a slight cost of distortion, and also leaves one less
# paramaeter that requires tuning.
if not onlyCTC:
loss = tf.reduce_mean((self.new_input-self.original)**2,axis=1)/regularizer + ctcLoss
else:
loss = ctcLoss
self.expanded_loss = tf.constant(0)
elif loss_fn == "CTCPSYCLIP":
target = ctc_label_dense_to_sparse(self.target_phrase, self.target_phrase_lengths, batch_size)
ctcLoss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits, sequence_length=lengths)
loss = ctcLoss
self.expanded_loss = tf.constant(0)
elif loss_fn == "CW":
raise NotImplemented("The current version of this project does not include the CW loss function implementation.")
else:
raise
self.deltaPSD = deltaPSD = tfPSD(self.new_input-self.original, window_size, step_per_window, self.psdMaxes)
psyLoss = tf.reduce_max(deltaPSD - self.psyTh, axis=[1,2])
self.loss = loss
self.psyLoss = tf.transpose(psyLoss)
self.ctcLoss = ctcLoss
def __init__(self,
sess,
phrase_length,
max_audio_len,
psdMaxes,
learning_rate=10,
num_iterations=5000,
window_size=256,
step_per_window=2,
batch_size=1,
mp3=False,
delta=None,
audio=None,
psdShape=None):
"""
Set up the attack procedure.
Here we create the TF graph that we're going to use to
actually generate the adversarial examples.
"""
self.sess = sess
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.batch_size = batch_size
self.phrase_length = phrase_length
self.max_audio_len = max_audio_len
self.mp3 = mp3
self.psdMaxes = psdMaxes
self.window_size = window_size
self.step_per_window = step_per_window
# Create all the variables necessary
# they are prefixed with qq_ just so that we know which
# ones are ours so when we restore the session we don't
# clobber them.
frame_length = int(window_size)
frame_step = int(window_size // step_per_window)
fft_length = int(2**np.ceil(np.log2(frame_length)))
sample_rate = 16000 # datapoints per second
freq_res = sample_rate / window_size
# sample_rate/2 is the maximal recorded frequency,
# We have window_size/2+1 frequencies
time_res = frame_step / (sample_rate / 1000)
# (sample_rate/1000) = samples per millisecond
# frame_step/(sample_rate/1000) => milliseconds for one step
self.regularizer = regularizer = tf.Variable(np.zeros(
(batch_size), dtype=np.float32),
name='qq_regularizer')
self.psyTh = psyTh = tf.Variable(np.zeros(
(batch_size, psdShape[0], psdShape[1]), dtype=np.float32),
name='qq_psyTh')
if (delta is None):
self.delta = delta = tf.Variable(np.zeros(
(batch_size, max_audio_len)).astype(np.float32) / 2,
name='qq_delta')
else:
self.delta = delta = tf.Variable(
(delta - audio).astype(np.float32), name='qq_delta')
self.mask = mask = tf.Variable(np.zeros((batch_size, max_audio_len),
dtype=np.float32),
name='qq_mask')
self.original = original = tf.Variable(np.zeros(
(batch_size, max_audio_len), dtype=np.float32),
name='qq_original')
self.lengths = lengths = tf.Variable(np.zeros(batch_size,
dtype=np.int32),
name='qq_lengths')
self.target_phrase = tf.Variable(np.zeros((batch_size, phrase_length),
dtype=np.int32),
name='qq_phrase')
self.target_phrase_lengths = tf.Variable(np.zeros((batch_size),
dtype=np.int32),
name='qq_phrase_lengths')
self.apply_delta = apply_delta = self.clipBatch(
delta, psyTh, regularizer, psdMaxes, max_audio_len, window_size,
step_per_window)
self.new_input = new_input = self.apply_delta * mask + original
# We set the new input to the model to be the above delta
# plus a mask, which allows us to enforce that certain
# values remain constant 0 for length padding sequences.
# Feed this final value to get the logits.
self.logits = logits = get_logits(new_input, lengths)
# And finally restore the graph to make the classifier
# actually do something interesting.
saver = tf.train.Saver(
[x for x in tf.global_variables() if 'qq' not in x.name])
saver.restore(sess, "models/session_dump")
target = ctc_label_dense_to_sparse(self.target_phrase,
self.target_phrase_lengths,
batch_size)
ctcLoss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=logits,
sequence_length=lengths)
loss = ctcLoss
self.expanded_loss = tf.constant(0)
self.deltaPSD = deltaPSD = tfPSD(self.new_input - self.original,
window_size, step_per_window,
self.psdMaxes)
self.loss = loss
self.psyLoss = tf.reduce_max(deltaPSD - self.psyTh, axis=[1, 2])
self.ctcLoss = ctcLoss
# Set up the Adam optimizer to perform gradient descent for us
start_vars = set(x.name for x in tf.global_variables())
optimizer = tf.train.AdamOptimizer(learning_rate)
grad, var = optimizer.compute_gradients(self.loss, [delta])[0]
self.train = optimizer.apply_gradients([(grad, var)])
end_vars = tf.global_variables()
new_vars = [x for x in end_vars if x.name not in start_vars]
sess.run(tf.variables_initializer(new_vars + [delta]))
# Decoder from the logits, to see how we're doing
self.decoded, _ = tf.nn.ctc_beam_search_decoder(logits,
lengths,
merge_repeated=False,
beam_width=100)
def next_batch(self):
uttids, source, source_lengths, target, target_lengths = self._queue.dequeue_many(
self._data_set.batch_size)
sparse_labels = ctc_label_dense_to_sparse(target, target_lengths,
self._data_set.batch_size)
return uttids, source, source_lengths, sparse_labels
def __init__(self, sess, loss_fn, phrase_length, max_audio_len,
learning_rate=10, num_iterations=5000, batch_size=1,
mp3=False, l2penalty=float('inf'), beam_width=100):
"""
Set up the attack procedure.
Here we create the TF graph that we're going to use to
actually generate the adversarial examples.
"""
self.sess = sess
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.batch_size = batch_size
self.phrase_length = phrase_length
self.max_audio_len = max_audio_len
self.mp3 = mp3
self.beam_width = beam_width
# Create all the variables necessary
# they are prefixed with qq_ just so that we know which
# ones are ours so when we restore the session we don't
# clobber them.
bs_mal_shape = [batch_size, max_audio_len]
bs_pl_shape = [batch_size, phrase_length]
self.delta = delta = tf.get_variable('qq_delta',
bs_mal_shape,
dtype=np.float32,
initializer=tf.zeros_initializer)
self.mask = mask = tf.get_variable('qq_mask',
bs_mal_shape,
dtype=np.float32,
initializer=tf.zeros_initializer)
self.cwmask = cwmask = tf.get_variable('qq_cwmask',
bs_pl_shape
dtype=np.float32,
initializer=tf.zeros_initializer)
self.original = original = tf.get_variable('qq_original',
bs_mal_shape,
dtype=np.float32,
initializer=tf.zeros_initializer)
self.lengths = tf.get_variable('qq_lengths',
np.zeros(batch_size,
dtype=np.int32)
initializer=tf.zeros_initializer)
self.importance = tf.get_variable('qq_importance',
bs_pl_shape,
dtype=np.float32,
initializer=tf.zeros_initializer)
self.target_phrase = tf.get_variable('qq_phrase',
bs_pl_shape,
dtype=np.int32,
initializer=tf.zeros_initializer)
self.target_phrase_lengths = tf.get_variable('qq_phrase_lengths',
[batch_size],
dtype=np.int32,
initializer=tf.zeros_initializer)
self.rescale = tf.get_variable('qq_phrase_lengths',
[batch_size,1],
dtype=np.float32,
initializer=tf.zeros_initializer)
# Initially we bound the l_infty norm by 2000, increase this
# constant if it's not big enough of a distortion for your dataset.
self.apply_delta = tf.clip_by_value(delta, -2000, 2000)*self.rescale
# We set the new input to the model to be the abve delta
# plus a mask, which allows us to enforce that certain
# values remain constant 0 for length padding sequences.
self.new_input = self.apply_delta*mask + original
# We add a tiny bit of noise to help make sure that we can
# clip our values to 16-bit integers and not break things.
noise = tf.random_normal(self.new_input.shape, stddev=2)
pass_in = tf.clip_by_value(self.new_input + noise, -2**15, 2**15-1)
# Feed this final value to get the logits.
self.logits = get_logits(pass_in, self.lengths)
# And finally restore the graph to make the classifier
# actually do something interesting.
saver = tf.train.Saver([x for x in tf.global_variables() if 'qq' not in x.name])
saver.restore(sess, "models/session_dump")
# Choose the loss function we want -- either CTC or CW
self.loss_fn = loss_fn
if loss_fn == "CTC":
target = ctc_label_dense_to_sparse(self.target_phrase, self.target_phrase_lengths, batch_size)
self.ctcloss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32),
inputs=self.logits, sequence_length=self.lengths)
# Slight hack: an infinite l2 penalty means that we don't penalize l2 distortion
# The code runs faster at a slight cost of distortion, and also leaves one less
# paramaeter that requires tuning.
if not np.isinf(l2penalty):
self.loss = tf.reduce_mean((self.new_input - self.original)**2,axis=1) + l2penalty * self.ctcloss
else:
self.loss = self.ctcloss
self.expanded_loss = tf.constant(0)
elif loss_fn == "CW":
raise NotImplemented("The current version of this project does not include the CW loss function implementation.")
else:
raise
# Set up the Adam optimizer to perform gradient descent for us
start_vars = set(x.name for x in tf.global_variables())
optimizer = tf.train.AdamOptimizer(self.learning_rate)
grad,var = optimizer.compute_gradients(self.loss, [delta])[0]
self.train = optimizer.apply_gradients([(tf.sign(grad),var)])
end_vars = tf.global_variables()
new_vars = [x for x in end_vars if x.name not in start_vars]
sess.run(tf.variables_initializer(new_vars + [delta]))
# Decoder from the logits, to see how we're doing
self.decoded, _ = tf.nn.ctc_beam_search_decoder(self.logits, self.lengths, merge_repeated=False, beam_width=self.beam_width)