<p>You can start by reading this paper: <a href="https://arxiv.org/abs/1412.6572" rel="nofollow noreferrer">https://arxiv.org/abs/1412.6572</a> (for example)</p>

<p>It explains one of the ways to generate adversarial examples by computing gradients of the loss function with respect to inputs. </p>

<p>Have a look at <a href="https://www.tensorflow.org/versions/master/api_docs/python/train/gradient_computation" rel="nofollow noreferrer"><code>tf.gradients()</code></a></p>

<p>Once you have defined your loss function, which is for example cross entropy, you do something like:</p>

<pre><code>grads = tf.gradients(loss, [x])[0]
signs = tf.sign(grads)
epsilon = tf.constant(0.25)
x_adversarial = tf.add(tf.multiply(epsilon, signs), x)
</code></pre>

<p><code>x_adversarial</code> will be your sneaky image. You can play with the <code>epsilon</code> value, which sets the magnitude of the added noise. </p>
Machine Learning (Adversarial Images)
<p>In case any of you don't know, adversarial images are images that belong to a certain class, but then are distorted without any visually perceptive difference to the human eye, but the network misunderstandingly recognizes it in a completely different class.</p>

<p>More information about it here:
<a href="http://karpathy.github.io/2015/03/30/breaking-convnets/" rel="nofollow noreferrer">http://karpathy.github.io/2015/03/30/breaking-convnets/</a></p>

<p>Using TensorFlow, I have learned a lot about convolutional neural networks.</p>

<pre><code>def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
</code></pre>

<p>The challenge is to input an image of the number 2 also labelled as '2', and somehow convolving this image so that the output would identify it as '6', changing the pixels so slightly that the difference is unrecognizable. </p>

<p>Anyone have any idea where to start with this?</p>