  Deep neural networks are known to be vulnerable to adversarial examples,
i.e., images that are maliciously perturbed to fool the model. Generating
adversarial examples has been mostly limited to finding small perturbations
that maximize the model prediction error. Such images, however, contain
artificial perturbations that make them somewhat distinguishable from natural
images. This property is used by several defense methods to counter adversarial
examples by applying denoising filters or training the model to be robust to
small perturbations.
  In this paper, we introduce a new class of adversarial examples, namely
"Semantic Adversarial Examples," as images that are arbitrarily perturbed to
fool the model, but in such a way that the modified image semantically
represents the same object as the original image. We formulate the problem of
generating such images as a constrained optimization problem and develop an
adversarial transformation based on the shape bias property of human cognitive
system. In our method, we generate adversarial images by first converting the
RGB image into the HSV (Hue, Saturation and Value) color space and then
randomly shifting the Hue and Saturation components, while keeping the Value
component the same. Our experimental results on CIFAR10 dataset show that the
accuracy of VGG16 network on adversarial color-shifted images is 5.7%.
