def get_ascad_trace_set(name, data, meta, limit=None):
"""
Convert ASCAD data to a TraceSet object.
"""
data_x, data_y = data
traces = []
plaintexts = []
keys = []
masks = []
limit = len(data_x) if limit is None else min(len(data_x), limit)
for i in range(0, limit):
traces.append(data_x[i])
plaintexts.append(meta[i]['plaintext'])
keys.append(meta[i]['key'])
masks.append(meta[i]['masks'])
traces = np.array(traces)
plaintexts = np.array(plaintexts)
keys = np.array(keys)
masks = np.array(masks)
trace_set = TraceSet(name='ascad-%s' % name, traces=traces, plaintexts=plaintexts, ciphertexts=None, keys=keys, masks=masks)
trace_set.window = Window(begin=0, end=len(trace_set.traces[0].signal))
trace_set.windowed = True
return trace_set
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if epoch % self.metric_freq != 0 or epoch == 0:
return
if self.trace_set is not None:
# Fetch inputs from trace_set
x = AIInput(self.conf).get_trace_set_inputs(self.trace_set)
if self.cnn:
x = np.expand_dims(x, axis=-1)
encodings = self.model.predict(x) # Output: [?, 16]
# Store encodings as fake traceset
keys = np.array([trace.key for trace in self.trace_set.traces])
plaintexts = np.array(
[trace.plaintext for trace in self.trace_set.traces])
fake_ts = TraceSet(traces=encodings,
plaintexts=plaintexts,
keys=keys,
name="fake_ts")
fake_ts.window = Window(begin=0, end=encodings.shape[1])
fake_ts.windowed = True
for i in range(self.key_low, self.key_high):
if len(set(keys[:, i])) > 1:
print(
"Warning: nonidentical key bytes detected. Skipping rank calculation"
)
print("Subkey %d:" % i)
print(keys[:, i])
break
rank, confidence = calculate_traceset_rank(
fake_ts, i, keys[0][i], self.conf
) # TODO: It is assumed here that all true keys of the test set are the same
self._save_best_rank_model(rank, confidence)
logs['rank %d' % i] = rank
logs['confidence %d' % i] = confidence
#self._save_best_rank_model(np.mean(ranks))
else:
print("Warning: no trace_set supplied to RankCallback")