adv_x[0, 0, row, col] < clip_max])
else:
search_domain = set([(row, col) for row in xrange(FLAGS.img_rows)
for col in xrange(FLAGS.img_cols)
if adv_x[0, 0, row, col] > clip_min])
// Initial the loop variables
iteration = 0
After Change
and len(search_domain) > 0):
// Compute the Jacobian components
grads_target, grads_others = jacobian(sess, x, grads, target, np.reshape(adv_x, original_shape), nb_features)
// Compute the saliency map for each of our target classes
// and return the two best candidate features for perturbation
