Show HN: Generating Collisions on NeuralHash

	import tensorflow as tf #We need tensorflow 2.x
	import numpy as np
	#The hashlength in bits
	hashLength = 256
	def buildModel():
	#we can set the seed to simulate the fact that this network is known and doesn't change between runs
	#tf.random.set_seed(42)
	model = tf.keras.Sequential()
	model.add(tf.keras.Input(shape=(1000)))
	model.add(tf.keras.layers.Dense(300,activation=tf.nn.selu))
	model.add(tf.keras.layers.Dense(300, activation=tf.nn.selu))
	model.add(tf.keras.layers.Dense(300, activation=tf.nn.selu))
	#The last layer contains the LSH Random hyperplanes
	model.add(tf.keras.layers.Dense(hashLength))
	return model
	#This use Random projection LSH (aka "HyperPlane LSH") on the features generated by the model
	def computeNeuralHash(m, img):
	hash = m(img).numpy()[0]
	targetstringhash = "".join(["1" if x > 0 else "0" for x in hash])
	return targetstringhash
	def demo( ):
	m = buildModel()
	#np.random.seed(340)
	targetimg = np.expand_dims(np.random.randn(1000),0)
	print(targetimg.shape)
	targetstringhash = computeNeuralHash(m,targetimg)
	print("targetstringhash : ")
	print(targetstringhash)
	flip = [ 1.0 if x=="0" else -1.0 for x in targetstringhash]
	print( "flip : ")
	print(flip)
	img = np.expand_dims(np.random.randn(1000), 0)
	#to make sure the hash is more stable we add a gap
	gap = 0.1
	#when the network is trying to have a 1 for the kth bit, it will try to have the feature in the range [gap, +infinity]
	#when the network is trying to have a 0 for the kth bit, it will try to have the feature in the range [-infinity,-gap]
	#Otherwise it get penalized
	loss = 1.0 #we initialize loss so that we take at least one iteration
	#we use a standard gradient descent
	learning_rate = 1e-2
	#we can do better using l-bfgs-b optimizer and handle bounds constraints
	#we can also add some additional loss to make the result similar to a provided image
	#or use a gan-loss to make it look "natural"
	while( loss > gap*gap ):
	loss = distanceBetweenHashes( m, img, flip, gap ).numpy()
	print("loss : ")
	print(loss)
	grad = gradient( m,img, flip,gap)
	img -= learning_rate * grad
	imgstringhash = computeNeuralHash(m,img)
	print("img : ")
	print( img )
	#This is not zero : We have found a totally different image
	print("targetimg - img : ")
	print(targetimg - img)
	print("targetstringhash : ")
	print(targetstringhash)
	print("imgstringhash : ")
	print( imgstringhash)
	# We should get True if a collision has been successfully produced
	print("targetstringhash == imgstringhash : ")
	print(targetstringhash == imgstringhash )
	def distanceBetweenHashes( model, input, flip , gap ):
	loss = tf.nn.l2_loss(tf.nn.relu(model(input) * flip + gap) )
	return loss
	def gradient(model, x, flip,gap):
	input = tf.convert_to_tensor(x, dtype=tf.float32)
	with tf.GradientTape() as t:
	t.watch(input)
	loss = distanceBetweenHashes( model, input,flip,gap)
	return t.gradient(loss, input).numpy()
	if __name__ == "__main__":
	demo()

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