Preprocessing as a step to attacks

[AkashGanesan]

Hi,

I was wondering if we can hooks that will allow us to preprocess the adversarial patterns generated before running the inference.  I do know that blur filtering helps in defending against FGSM though fails against C&W attacks (https://arxiv.org/abs/1612.07767) and I was wondering if we can actually take advantage of a myriad of image processing/de-noising procedures.  If we don't have something like that, I'd be happy to contribute to that.

Thank you.

Akash G

[Ian Goodfellow]

The easiest way to do this is to just put it in Model.fprop.

You can write Models that wrap other Models and add preprocessing.

Here's an example that does test-time data augmentation with multiple crops and flips:

class Augmentor(Model):

  def __init__(self, raw):

    self.raw = raw

  def get_params(self):

    return self.raw.get_params()

  def fprop(self, x):

    mode = "REFLECT"

    assert mode in 'REFLECT SYMMETRIC CONSTANT'.split()

    pad = [2, 2]

    def _pad(img):

      return tf.pad(img, [[pad[0], pad[0]], [pad[1], pad[1]], [0, 0]], mode)

    xp = tf.map_fn(_pad, x)

    xs = []

    for i in xrange(pad[0] * 2):

      for j in xrange(pad[1] * 2):

        xs.append(tf.slice(xp, [0, i, j, 0], tf.shape(x)))

        with tf.device("/CPU:0"):

          xs.append(tf.image.flip_left_right(xs[-1]))

    @function.Defun(tf.float32)

    def f(xarg):

      xarg.set_shape(x.get_shape())

      return self.raw.get_logits(xarg)

    logits = [f(e) for e in xs]

    logits = tf.add_n(logits) / len(logits)

    return {'logits': logits}

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