# # Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import numpy as np import build.lc3 as lc3 import tables as T, appendix_c as C ### ------------------------------------------------------------------------ ### class AttackDetector: def __init__(self, dt, sr): self.dt = dt self.sr = sr self.ms = T.DT_MS[dt] self.xn1 = 0 self.xn2 = 0 self.en1 = 0 self.an1 = 0 self.p_att = 0 def is_enabled(self, nbytes): c1 = self.dt == T.DT_10M and \ self.sr == T.SRATE_32K and nbytes > 80 c2 = self.dt == T.DT_10M and \ self.sr >= T.SRATE_48K and nbytes >= 100 c3 = self.dt == T.DT_7M5 and \ self.sr == T.SRATE_32K and nbytes >= 61 and nbytes < 150 c4 = self.dt == T.DT_7M5 and \ self.sr >= T.SRATE_48K and nbytes >= 75 and nbytes < 150 return c1 or c2 or c3 or c4 def run(self, nbytes, x): ### 3.3.6.2 Downsampling and filtering input mf = int(16 * self.ms) r = len(x) // mf x_att = np.array([ np.sum(x[i*r:(i+1)*r]) for i in range(mf) ]) x_hp = np.empty(mf) x_hp[0 ] = 0.375 * x_att[0 ] - 0.5 * self.xn1 + 0.125 * self.xn2 x_hp[1 ] = 0.375 * x_att[1 ] - 0.5 * x_att[0 ] + 0.125 * self.xn1 x_hp[2:] = 0.375 * x_att[2:] - 0.5 * x_att[1:-1] + 0.125 * x_att[0:-2] self.xn2 = x_att[-2] self.xn1 = x_att[-1] ### 3.3.6.3 Energy calculation nb = int(self.ms / 2.5) e_att = np.array([ np.sum(np.square(x_hp[40*i:40*(i+1)])) for i in range(nb) ]) a_att = np.empty(nb) a_att[0] = np.maximum(0.25 * self.an1, self.en1) for i in range(1,nb): a_att[i] = np.maximum(0.25 * a_att[i-1], e_att[i-1]) self.en1 = e_att[-1] self.an1 = a_att[-1] ### 3.3.6.4 Attack Detection p_att = -1 flags = [ (e_att[i] > 8.5 * a_att[i]) for i in range(nb) ] for (i, f) in enumerate(flags): if f: p_att = i f_att = p_att >= 0 or self.p_att - 1 >= nb // 2 self.p_att = 1 + p_att return self.is_enabled(nbytes) and f_att def initial_state(): return { 'en1': 0.0, 'an1': 0.0, 'p_att': 0 } ### ------------------------------------------------------------------------ ### def check_enabling(rng, dt): ok = True for sr in range(T.SRATE_16K, T.NUM_SRATE): attdet = AttackDetector(dt, sr) for nbytes in [ 61, 61-1, 75-1, 75, 80, 80+1, 100-1, 100, 150-1, 150 ]: f_att = lc3.attdet_run(dt, sr, nbytes, initial_state(), 2 * rng.random(T.NS[dt][sr]+6) - 1) ok = ok and f_att == attdet.is_enabled(nbytes) return ok def check_unit(rng, dt, sr): ns = T.NS[dt][sr] ok = True attdet = AttackDetector(dt, sr) state_c = initial_state() x_c = np.zeros(ns+6) for run in range(100): ### Generate noise, and an attack at random point x = ((2 * rng.random(ns)) - 1) * (2 ** 8 - 1) x[(ns * rng.random()).astype(int)] *= 2 ** 7 ### Check Implementation f_att = attdet.run(100, x) x_c = np.append(x_c[-6:], x) f_att_c = lc3.attdet_run(dt, sr, 100, state_c, x_c) ok = ok and f_att_c == f_att ok = ok and np.amax(np.abs(1 - state_c['en1']/attdet.en1)) < 2 ok = ok and np.amax(np.abs(1 - state_c['an1']/attdet.an1)) < 2 ok = ok and state_c['p_att'] == attdet.p_att return ok def check_appendix_c(dt): sr = T.SRATE_48K state = initial_state() x = np.append(np.zeros(6), C.X_PCM_ATT[dt][0]) f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[dt], state, x) ok = f_att == C.F_ATT[dt][0] x = np.append(x[-6:], C.X_PCM_ATT[dt][1]) f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[dt], state, x) ok = f_att == C.F_ATT[dt][1] return ok def check(): rng = np.random.default_rng(1234) ok = True for dt in range(T.NUM_DT): ok and check_enabling(rng, dt) for dt in range(T.NUM_DT): for sr in range(T.SRATE_32K, T.NUM_SRATE): ok = ok and check_unit(rng, dt, sr) for dt in range(T.NUM_DT): ok = ok and check_appendix_c(dt) return ok ### ------------------------------------------------------------------------ ###