text: ## l2_attack.py -- attack a network optimizing for l_2 distance
  ##
  ## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
  ##
  ## This program is licenced under the BSD 2-Clause licence,
  ## contained in the LICENCE file in this directory.
  
  import sys
  import tensorflow as tf
  import numpy as np
  import time
  
  BINARY_SEARCH_STEPS = 1  # number of times to adjust the constant with binary search
  MAX_ITERATIONS = 10000   # number of iterations to perform gradient descent
  ABORT_EARLY = True       # if we stop improving, abort gradient descent early
  LEARNING_RATE = 2e-3     # larger values converge faster to less accurate results
  TARGETED = True          # should we target one specific class? or just be wrong?
  CONFIDENCE = 0           # how strong the adversarial example should be
  INITIAL_CONST = 0.01     # the initial constant c to pick as a first guess
  
  class CarliniL2:
      def __init__(self, sess, model, batch_size=1, confidence = CONFIDENCE,
                   targeted = TARGETED, learning_rate = LEARNING_RATE,
                   binary_search_steps = BINARY_SEARCH_STEPS, max_iterations = MAX_ITERATIONS, print_every = 100, early_stop_iters = 0,
                   abort_early = ABORT_EARLY, 
                   initial_const = INITIAL_CONST,
                   use_log = False, adam_beta1 = 0.9, adam_beta2 = 0.999):
          """
          The L_2 optimized attack. 
  
          This attack is the most efficient and should be used as the primary 
          attack to evaluate potential defenses.
  
          Returns adversarial examples for the supplied model.
  
          confidence: Confidence of adversarial examples: higher produces examples
            that are farther away, but more strongly classified as adversarial.
          batch_size: Number of attacks to run simultaneously.
          targeted: True if we should perform a targetted attack, False otherwise.
          learning_rate: The learning rate for the attack algorithm. Smaller values
            produce better results but are slower to converge.
          binary_search_steps: The number of times we perform binary search to
            find the optimal tradeoff-constant between distance and confidence. 
          max_iterations: The maximum number of iterations. Larger values are more
            accurate; setting too small will require a large learning rate and will
            produce poor results.
          abort_early: If true, allows early aborts if gradient descent gets stuck.
          initial_const: The initial tradeoff-constant to use to tune the relative
            importance of distance and confidence. If binary_search_steps is large,
            the initial constant is not important.
          """
  
          image_size, num_channels, num_labels = model.image_size, model.num_channels, model.num_labels
          self.sess = sess
          self.TARGETED = targeted
          self.LEARNING_RATE = learning_rate
          self.MAX_ITERATIONS = max_iterations
          self.print_every = print_every
          self.early_stop_iters = early_stop_iters if early_stop_iters != 0 else max_iterations // 10
          print("early stop:", self.early_stop_iters)
          self.BINARY_SEARCH_STEPS = binary_search_steps
          self.ABORT_EARLY = abort_early
          self.CONFIDENCE = confidence
          self.initial_const = initial_const
          self.batch_size = batch_size
  
          self.repeat = binary_search_steps >= 10
  
          shape = (batch_size,image_size,image_size,num_channels)
          
          # the variable we're going to optimize over
          self.modifier = tf.Variable(np.zeros(shape,dtype=np.float32))
          # self.modifier = tf.Variable(np.load('black_iter_350.npy').astype(np.float32).reshape(shape))
  
          # these are variables to be more efficient in sending data to tf
          self.timg = tf.Variable(np.zeros(shape), dtype=tf.float32)
          self.tlab = tf.Variable(np.zeros((batch_size,num_labels)), dtype=tf.float32)
          self.const = tf.Variable(np.zeros(batch_size), dtype=tf.float32)
  
          # and here's what we use to assign them
          self.assign_timg = tf.placeholder(tf.float32, shape)
          self.assign_tlab = tf.placeholder(tf.float32, (batch_size,num_labels))
          self.assign_const = tf.placeholder(tf.float32, [batch_size])
          
          # the resulting image, tanh'd to keep bounded from -0.5 to 0.5
          self.newimg = tf.tanh(self.modifier + self.timg)/2
          
          # prediction BEFORE-SOFTMAX of the model
          self.output = model.predict(self.newimg)
          
          # distance to the input data
          self.l2dist = tf.reduce_sum(tf.square(self.newimg-tf.tanh(self.timg)/2),[1,2,3])
          
          # compute the probability of the label class versus the maximum other
          self.real = tf.reduce_sum((self.tlab)*self.output,1)
          self.other = tf.reduce_max((1-self.tlab)*self.output - (self.tlab*10000),1)
  
          if self.TARGETED:
              if use_log:
                  # loss1 = tf.maximum(- tf.log(self.other), - tf.log(self.real))
                  # loss1 = - tf.log(self.real)
                  loss1 = tf.maximum(0.0, tf.log(self.other + 1e-30) - tf.log(self.real + 1e-30))
              else:
                  # if targetted, optimize for making the other class most likely
                  loss1 = tf.maximum(0.0, self.other-self.real+self.CONFIDENCE)
          else:
              if use_log:
                  # loss1 = tf.log(self.real)
                  loss1 = tf.maximum(0.0, tf.log(self.real + 1e-30) - tf.log(self.other + 1e-30))
              else:
              # if untargeted, optimize for making this class least likely.
                  loss1 = tf.maximum(0.0, self.real-self.other+self.CONFIDENCE)
  
          # sum up the losses
          self.loss2 = tf.reduce_sum(self.l2dist)
          self.loss1 = tf.reduce_sum(self.const*loss1)
          self.loss = self.loss1+self.loss2
          
          # Setup the adam optimizer and keep track of variables we're creating
          start_vars = set(x.name for x in tf.global_variables())
          # optimizer = tf.train.GradientDescentOptimizer(self.LEARNING_RATE)
          # optimizer = tf.train.MomentumOptimizer(self.LEARNING_RATE, 0.99)
          # optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE)
          # optimizer = tf.train.AdadeltaOptimizer(self.LEARNING_RATE)
          optimizer = tf.train.AdamOptimizer(self.LEARNING_RATE, adam_beta1, adam_beta2)
          self.train = optimizer.minimize(self.loss, var_list=[self.modifier])
          end_vars = tf.global_variables()
          new_vars = [x for x in end_vars if x.name not in start_vars]
  
          # these are the variables to initialize when we run
          self.setup = []
          self.setup.append(self.timg.assign(self.assign_timg))
          self.setup.append(self.tlab.assign(self.assign_tlab))
          self.setup.append(self.const.assign(self.assign_const))
          # self.grad_op = tf.gradients(self.loss, self.modifier)
          
          self.init = tf.variables_initializer(var_list=[self.modifier]+new_vars)
  
      def attack(self, imgs, targets):
          """
          Perform the L_2 attack on the given images for the given targets.
  
          If self.targeted is true, then the targets represents the target labels.
          If self.targeted is false, then targets are the original class labels.
          """
          r = []
          print('go up to',len(imgs))
          for i in range(0,len(imgs),self.batch_size):
              print('tick',i)
              r.extend(self.attack_batch(imgs[i:i+self.batch_size], targets[i:i+self.batch_size])[0])
          return np.array(r)
  
      def attack_batch(self, imgs, labs):
          """
          Run the attack on a batch of images and labels.
          """
          def compare(x,y):
              if not isinstance(x, (float, int, np.int64)):
                  x = np.copy(x)
                  if self.TARGETED:
                      x[y] -= self.CONFIDENCE
                  else:
                      x[y] += self.CONFIDENCE
                  x = np.argmax(x)
              if self.TARGETED:
                  return x == y
              else:
                  return x != y
  
          batch_size = self.batch_size
  
          # convert to tanh-space
          imgs = np.arctanh(imgs*1.999999)
  
          # set the lower and upper bounds accordingly
          lower_bound = np.zeros(batch_size)
          CONST = np.ones(batch_size)*self.initial_const
          upper_bound = np.ones(batch_size)*1e10
  
          # the best l2, score, and image attack
          o_bestl2 = [1e10]*batch_size
          o_bestscore = [-1]*batch_size
          o_bestattack = [np.zeros(imgs[0].shape)]*batch_size
          o_best_const = [self.initial_const]*batch_size
          
          for outer_step in range(self.BINARY_SEARCH_STEPS):
              print("current best l2", o_bestl2)
              # completely reset adam's internal state.
              self.sess.run(self.init)
              batch = imgs[:batch_size]
              batchlab = labs[:batch_size]
      
              bestl2 = [1e10]*batch_size
              bestscore = [-1]*batch_size
  
              # The last iteration (if we run many steps) repeat the search once.
              if self.repeat == True and outer_step == self.BINARY_SEARCH_STEPS-1:
                  CONST = upper_bound
  
              # set the variables so that we don't have to send them over again
              self.sess.run(self.setup, {self.assign_timg: batch,
                                         self.assign_tlab: batchlab,
                                         self.assign_const: CONST})
              
              prev = 1e6
              train_timer = 0.0
              for iteration in range(self.MAX_ITERATIONS):
                  # print out the losses every 10%
                  if iteration%(self.MAX_ITERATIONS//self.print_every) == 0:
                      # print(iteration,self.sess.run((self.loss,self.real,self.other,self.loss1,self.loss2)))
                      # grad = self.sess.run(self.grad_op)
                      # old_modifier = self.sess.run(self.modifier)
                      # np.save('white_iter_{}'.format(iteration), modifier)
                      loss, real, other, loss1, loss2 = self.sess.run((self.loss,self.real,self.other,self.loss1,self.loss2))
                      print("[STATS][L2] iter = {}, time = {:.3f}, loss = {:.5g}, real = {:.5g}, other = {:.5g}, loss1 = {:.5g}, loss2 = {:.5g}".format(iteration, train_timer, loss, real[0], other[0], loss1, loss2))
                      sys.stdout.flush()
  
                  attack_begin_time = time.time()
                  # perform the attack 
                  _, l, l2s, scores, nimg = self.sess.run([self.train, self.loss, 
                                                           self.l2dist, self.output, 
                                                           self.newimg])
  
                  new_modifier = self.sess.run(self.modifier)
                  
                  # print(grad[0].reshape(-1))
                  # print((old_modifier - new_modifier).reshape(-1))
  
                  # check if we should abort search if we're getting nowhere.
                  if self.ABORT_EARLY and iteration % self.early_stop_iters == 0:
                      if l > prev*.9999:
                          print("Early stopping because there is no improvement")
                          break
                      prev = l
  
                  # adjust the best result found so far
                  for e,(l2,sc,ii) in enumerate(zip(l2s,scores,nimg)):
                      if l2 < bestl2[e] and compare(sc, np.argmax(batchlab[e])):
                          bestl2[e] = l2
                          bestscore[e] = np.argmax(sc)
                      if l2 < o_bestl2[e] and compare(sc, np.argmax(batchlab[e])):
                          o_bestl2[e] = l2
                          o_bestscore[e] = np.argmax(sc)
                          o_bestattack[e] = ii
                          o_best_const[e] = CONST[e]
  
                  train_timer += time.time() - attack_begin_time
  
              # adjust the constant as needed
              for e in range(batch_size):
                  if compare(bestscore[e], np.argmax(batchlab[e])) and bestscore[e] != -1:
                      # modifier = self.sess.run(self.modifier)
                      # np.save("best.model", modifier)
                      print('old constant: ', CONST[e])
                      # success, divide const by two
                      upper_bound[e] = min(upper_bound[e],CONST[e])
                      if upper_bound[e] < 1e9:
                          CONST[e] = (lower_bound[e] + upper_bound[e])/2
                      print('new constant: ', CONST[e])
                  else:
                      print('old constant: ', CONST[e])
                      # failure, either multiply by 10 if no solution found yet
                      #          or do binary search with the known upper bound
                      lower_bound[e] = max(lower_bound[e],CONST[e])
                      if upper_bound[e] < 1e9:
                          CONST[e] = (lower_bound[e] + upper_bound[e])/2
                      else:
                          CONST[e] *= 10
                      print('new constant: ', CONST[e])
  
          # return the best solution found
          o_bestl2 = np.array(o_bestl2)
          return np.array(o_bestattack), o_best_const
  