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DLV.py
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#!/usr/bin/env python
"""
main file
author: Xiaowei Huang
"""
import sys
sys.path.append('networks')
sys.path.append('safety_check')
sys.path.append('configuration')
sys.path.append('basics')
sys.path.append('MCTS')
import time
import numpy as np
import copy
import random
import matplotlib.pyplot as plt
import matplotlib as mpl
from loadData import loadData
from regionSynth import regionSynth, initialiseRegion
from precisionSynth import precisionSynth
from safety_analysis import safety_analysis
from configuration import *
from basics import *
from networkBasics import *
from searchTree import searchTree
from searchMCTS import searchMCTS
from mcts_twoPlayer import mcts_twoPlayer
from initialiseSiftKeypoints import GMM
from dataCollection import dataCollection
from inputManipulation import applyManipulation,assignManipulationSimple
from keras import backend as K
def main():
model = loadData()
dc = dataCollection()
# handle a set of inputs starting from an index
succNum = 0
for whichIndex in range(startIndexOfImage,startIndexOfImage + dataProcessingBatchNum):
print "\n\nprocessing input of index %s in the dataset: " %(str(whichIndex))
succ = handleOne(model,dc,whichIndex)
if succ == True: succNum += 1
dc.addSuccPercent(succNum/float(dataProcessingBatchNum))
dc.provideDetails()
dc.summarise()
dc.close()
###########################################################################
#
# safety checking
# starting from the a specified hidden layer
#
############################################################################
## how many branches to expand
numOfPointsAfterEachFeature = 1
mcts_mode = "sift_twoPlayer"
#mcts_mode = "singlePlayer"
def handleOne(model,dc,startIndexOfImage):
# get an image to interpolate
global np
image = NN.getImage(model,startIndexOfImage)
print("the shape of the input is "+ str(image.shape))
if dataset == "twoDcurve": image = np.array([3.58747339,1.11101673])
dc.initialiseIndex(startIndexOfImage)
originalImage = copy.deepcopy(image)
if checkingMode == "stepwise":
k = startLayer
elif checkingMode == "specificLayer":
k = maxLayer
while k <= maxLayer:
layerType = getLayerType(model, k)
re = False
start_time = time.time()
# only these layers need to be checked
if layerType in ["Convolution2D","Conv2D", "Dense", "InputLayer"] and k >= 0 :
dc.initialiseLayer(k)
st = searchTree(image,k)
st.addImages(model,[image])
print("\n================================================================")
print "\nstart checking the safety of layer "+str(k)
(originalClass,originalConfident) = NN.predictWithImage(model,image)
origClassStr = dataBasics.LABELS(int(originalClass))
path0="%s/%s_original_as_%s_with_confidence_%s.png"%(directory_pic_string,startIndexOfImage,origClassStr,originalConfident)
dataBasics.save(-1,originalImage, path0)
# for every layer
f = 0
while f < numOfFeatures :
f += 1
print("\n================================================================")
print("Round %s of layer %s for image %s"%(f,k,startIndexOfImage))
index = st.getOneUnexplored()
imageIndex = copy.deepcopy(index)
# for every image
# start from the first hidden layer
t = 0
re = False
while True and index != (-1,-1):
# pick the first element of the queue
print "(1) get a manipulated input ..."
(image0,span,numSpan,numDimsToMani,_) = st.getInfo(index)
print "current layer: %s."%(t)
print "current index: %s."%(str(index))
path2 = directory_pic_string+"/temp.png"
print "current operated image is saved into %s"%(path2)
dataBasics.save(index[0],image0,path2)
print "(2) synthesise region from %s..."%(span.keys())
# ne: next region, i.e., e_{k+1}
#print "manipulated: %s"%(st.manipulated[t])
(nextSpan,nextNumSpan,numDimsToMani) = regionSynth(model,dataset,image0,st.manipulated[t],t,span,numSpan,numDimsToMani)
st.addManipulated(t,nextSpan.keys())
(nextSpan,nextNumSpan,npre) = precisionSynth(model,image0,t,span,numSpan,nextSpan,nextNumSpan)
print "dimensions to be considered: %s"%(nextSpan)
print "spans for the dimensions: %s"%(nextNumSpan)
if t == k:
# only after reaching the k layer, it is counted as a pass
print "(3) safety analysis ..."
# wk for the set of counterexamples
# rk for the set of images that need to be considered in the next precision
# rs remembers how many input images have been processed in the last round
# nextSpan and nextNumSpan are revised by considering the precision npre
(nextSpan,nextNumSpan,rs,wk,rk) = safety_analysis(model,dataset,t,startIndexOfImage,st,index,nextSpan,nextNumSpan,npre)
if len(rk) > 0:
rk = (zip (*rk))[0]
print "(4) add new images ..."
random.seed(time.time())
if len(rk) > numOfPointsAfterEachFeature:
rk = random.sample(rk, numOfPointsAfterEachFeature)
diffs = diffImage(image0,rk[0])
print("the dimensions of the images that are changed in the this round: %s"%diffs)
if len(diffs) == 0:
st.clearManipulated(k)
return
st.addImages(model,rk)
st.removeProcessed(imageIndex)
(re,percent,eudist,l1dist,l0dist) = reportInfo(image,wk)
print "euclidean distance %s"%(euclideanDistance(image,rk[0]))
print "L1 distance %s"%(l1Distance(image,rk[0]))
print "L0 distance %s"%(l0Distance(image,rk[0]))
print "manipulated percentage distance %s\n"%(diffPercent(image,rk[0]))
break
else:
st.removeProcessed(imageIndex)
break
else:
print "(3) add new intermediate node ..."
index = st.addIntermediateNode(image0,nextSpan,nextNumSpan,npre,numDimsToMani,index)
re = False
t += 1
if re == True:
dc.addManipulationPercentage(percent)
print "euclidean distance %s"%(eudist)
print "L1 distance %s"%(l1dist)
print "L0 distance %s"%(l0dist)
print "manipulated percentage distance %s\n"%(percent)
dc.addEuclideanDistance(eudist)
dc.addl1Distance(l1dist)
dc.addl0Distance(l0dist)
(ocl,ocf) = NN.predictWithImage(model,wk[0])
dc.addConfidence(ocf)
break
if f == numOfFeatures:
print "(6) no adversarial example is found in this layer within the distance restriction."
st.destructor()
elif layerType in ["Input"] and k < 0 and mcts_mode == "sift_twoPlayer" :
print "directly handling the image ... "
dc.initialiseLayer(k)
(originalClass,originalConfident) = NN.predictWithImage(model,image)
origClassStr = dataBasics.LABELS(int(originalClass))
path0="%s/%s_original_as_%s_with_confidence_%s.png"%(directory_pic_string,startIndexOfImage,origClassStr,originalConfident)
dataBasics.save(-1,originalImage, path0)
# initialise a search tree
st= mcts_twoPlayer(model,model,image,image,-1,"cooperator")
st.initialiseActions()
st.setManipulationType("sift_twoPlayer")
start_time_all = time.time()
runningTime_all = 0
numberOfMoves = 0
while st.terminalNode(st.rootIndex) == False and st.terminatedByControlledSearch(st.rootIndex) == False and runningTime_all <= MCTS_all_maximal_time:
print("the number of moves we have made up to now: %s"%(numberOfMoves))
eudist = st.euclideanDist(st.rootIndex)
l1dist = st.l1Dist(st.rootIndex)
l0dist = st.l0Dist(st.rootIndex)
percent = st.diffPercent(st.rootIndex)
diffs = st.diffImage(st.rootIndex)
print("euclidean distance %s"%(eudist))
print("L1 distance %s"%(l1dist))
print("L0 distance %s"%(l0dist))
print("manipulated percentage distance %s"%(percent))
print("manipulated dimensions %s"%(diffs))
start_time_level = time.time()
runningTime_level = 0
childTerminated = False
while runningTime_level <= MCTS_level_maximal_time:
(leafNode,availableActions) = st.treeTraversal(st.rootIndex)
newNodes = st.initialiseExplorationNode(leafNode,availableActions)
for node in newNodes:
(childTerminated, value) = st.sampling(node,availableActions)
#if childTerminated == True: break
st.backPropagation(node,value)
#if childTerminated == True: break
runningTime_level = time.time() - start_time_level
nprint("best possible one is %s"%(str(st.bestCase)))
bestChild = st.bestChild(st.rootIndex)
#st.collectUselessPixels(st.rootIndex)
st.makeOneMove(bestChild)
image1 = st.applyManipulationToGetImage(st.spans[st.rootIndex],st.numSpans[st.rootIndex])
diffs = st.diffImage(st.rootIndex)
path0="%s/%s_temp_%s.png"%(directory_pic_string,startIndexOfImage,len(diffs))
dataBasics.save(-1,image1,path0)
(newClass,newConfident) = NN.predictWithImage(model,image1)
print("confidence: %s"%(newConfident))
if childTerminated == True: break
# store the current best
(_,bestSpans,bestNumSpans) = st.bestCase
image1 = st.applyManipulationToGetImage(bestSpans,bestNumSpans)
path0="%s/%s_currentBest.png"%(directory_pic_string,startIndexOfImage)
dataBasics.save(-1,image1,path0)
numberOfMoves += 1
runningTime_all = time.time() - start_time_all
(_,bestSpans,bestNumSpans) = st.bestCase
#image1 = applyManipulation(st.image,st.spans[st.rootIndex],st.numSpans[st.rootIndex])
image1 = st.applyManipulationToGetImage(bestSpans,bestNumSpans)
(newClass,newConfident) = NN.predictWithImage(model,image1)
newClassStr = dataBasics.LABELS(int(newClass))
re = newClass != originalClass
if re == True:
path0="%s/%s_%s_%s_modified_into_%s_with_confidence_%s.png"%(directory_pic_string,startIndexOfImage,"sift_twoPlayer", origClassStr,newClassStr,newConfident)
dataBasics.save(-1,image1,path0)
path0="%s/%s_diff.png"%(directory_pic_string,startIndexOfImage)
dataBasics.save(-1,np.subtract(image,image1),path0)
print("\nfound an adversary image within prespecified bounded computational resource. The following is its information: ")
print("difference between images: %s"%(diffImage(image,image1)))
print("number of adversarial examples found: %s"%(st.numAdv))
eudist = euclideanDistance(st.image,image1)
l1dist = l1Distance(st.image,image1)
l0dist = l0Distance(st.image,image1)
percent = diffPercent(st.image,image1)
print("euclidean distance %s"%(eudist))
print("L1 distance %s"%(l1dist))
print("L0 distance %s"%(l0dist))
print("manipulated percentage distance %s"%(percent))
print("class is changed into %s with confidence %s\n"%(newClassStr, newConfident))
dc.addRunningTime(time.time() - start_time_all)
dc.addConfidence(newConfident)
dc.addManipulationPercentage(percent)
dc.addEuclideanDistance(eudist)
dc.addl1Distance(l1dist)
dc.addl0Distance(l0dist)
#path0="%s/%s_original_as_%s_heatmap.png"%(directory_pic_string,startIndexOfImage,origClassStr)
#plt.imshow(GMM(image),interpolation='none')
#plt.savefig(path0)
#path1="%s/%s_%s_%s_modified_into_%s_heatmap.png"%(directory_pic_string,startIndexOfImage,"sift_twoPlayer", origClassStr,newClassStr)
#plt.imshow(GMM(image1),interpolation='none')
#plt.savefig(path1)
else:
print("\nfailed to find an adversary image within prespecified bounded computational resource. ")
elif layerType in ["Input"] and k < 0 and mcts_mode == "singlePlayer" :
print "directly handling the image ... "
dc.initialiseLayer(k)
(originalClass,originalConfident) = NN.predictWithImage(model,image)
origClassStr = dataBasics.LABELS(int(originalClass))
path0="%s/%s_original_as_%s_with_confidence_%s.png"%(directory_pic_string,startIndexOfImage,origClassStr,originalConfident)
dataBasics.save(-1,originalImage, path0)
# initialise a search tree
st = searchMCTS(model,image,k)
st.initialiseActions()
start_time_all = time.time()
runningTime_all = 0
numberOfMoves = 0
while st.terminalNode(st.rootIndex) == False and st.terminatedByControlledSearch(st.rootIndex) == False and runningTime_all <= MCTS_all_maximal_time:
print("the number of moves we have made up to now: %s"%(numberOfMoves))
eudist = st.euclideanDist(st.rootIndex)
l1dist = st.l1Dist(st.rootIndex)
l0dist = st.l0Dist(st.rootIndex)
percent = st.diffPercent(st.rootIndex)
diffs = st.diffImage(st.rootIndex)
print "euclidean distance %s"%(eudist)
print "L1 distance %s"%(l1dist)
print "L0 distance %s"%(l0dist)
print "manipulated percentage distance %s"%(percent)
print "manipulated dimensions %s"%(diffs)
start_time_level = time.time()
runningTime_level = 0
childTerminated = False
while runningTime_level <= MCTS_level_maximal_time:
(leafNode,availableActions) = st.treeTraversal(st.rootIndex)
newNodes = st.initialiseExplorationNode(leafNode,availableActions)
for node in newNodes:
(childTerminated, value) = st.sampling(node,availableActions)
if childTerminated == True: break
st.backPropagation(node,value)
if childTerminated == True: break
runningTime_level = time.time() - start_time_level
print("best possible one is %s"%(st.showBestCase()))
bestChild = st.bestChild(st.rootIndex)
#st.collectUselessPixels(st.rootIndex)
st.makeOneMove(bestChild)
image1 = applyManipulation(st.image,st.spans[st.rootIndex],st.numSpans[st.rootIndex])
diffs = st.diffImage(st.rootIndex)
path0="%s/%s_temp_%s.png"%(directory_pic_string,startIndexOfImage,len(diffs))
dataBasics.save(-1,image1,path0)
(newClass,newConfident) = NN.predictWithImage(model,image1)
print "confidence: %s"%(newConfident)
if childTerminated == True: break
# store the current best
(_,bestSpans,bestNumSpans) = st.bestCase
image1 = applyManipulation(st.image,bestSpans,bestNumSpans)
path0="%s/%s_currentBest.png"%(directory_pic_string,startIndexOfImage)
dataBasics.save(-1,image1,path0)
runningTime_all = time.time() - runningTime_all
numberOfMoves += 1
(_,bestSpans,bestNumSpans) = st.bestCase
#image1 = applyManipulation(st.image,st.spans[st.rootIndex],st.numSpans[st.rootIndex])
image1 = applyManipulation(st.image,bestSpans,bestNumSpans)
(newClass,newConfident) = NN.predictWithImage(model,image1)
newClassStr = dataBasics.LABELS(int(newClass))
re = newClass != originalClass
path0="%s/%s_%s_modified_into_%s_with_confidence_%s.png"%(directory_pic_string,startIndexOfImage,origClassStr,newClassStr,newConfident)
dataBasics.save(-1,image1,path0)
#print np.max(image1), np.min(image1)
print("difference between images: %s"%(diffImage(image,image1)))
#plt.imshow(image1 * 255, cmap=mpl.cm.Greys)
#plt.show()
if re == True:
eudist = euclideanDistance(st.image,image1)
l1dist = l1Distance(st.image,image1)
l0dist = l0Distance(st.image,image1)
percent = diffPercent(st.image,image1)
print "euclidean distance %s"%(eudist)
print "L1 distance %s"%(l1dist)
print "L0 distance %s"%(l0dist)
print "manipulated percentage distance %s"%(percent)
print "class is changed into %s with confidence %s\n"%(newClassStr, newConfident)
dc.addEuclideanDistance(eudist)
dc.addl1Distance(l1dist)
dc.addl0Distance(l0dist)
dc.addManipulationPercentage(percent)
st.destructor()
else:
print("layer %s is of type %s, skipping"%(k,layerType))
#return
runningTime = time.time() - start_time
dc.addRunningTime(runningTime)
if re == True and exitWhen == "foundFirst":
break
k += 1
print("Please refer to the file %s for statistics."%(dc.fileName))
if re == True:
return True
else: return False
def reportInfo(image,wk):
# exit only when we find an adversarial example
if wk == []:
print "(5) no adversarial example is found in this round."
return (False,0,0,0,0)
else:
print "(5) an adversarial example has been found."
image0 = wk[0]
eudist = euclideanDistance(image,image0)
l1dist = l1Distance(image,image0)
l0dist = l0Distance(image,image0)
percent = diffPercent(image,image0)
return (True,percent,eudist,l1dist,l0dist)
if __name__ == "__main__":
start_time = time.time()
main()
print("--- %s seconds ---" % (time.time() - start_time))