  Deep neural networks are known to be vulnerable to adversarial perturbations.
The amount of these perturbations are generally quantified using $L_p$ metrics,
such as $L_0$, $L_2$ and $L_\infty$. However, even when the measured
perturbations are small, they tend to be noticeable by human observers since
$L_p$ distance metrics are not representative of human perception. On the other
hand, humans are less sensitive to changes in colorspace. In addition, pixel
shifts in a constrained neighborhood are hard to notice. Motivated by these
observations, we propose a method that creates adversarial examples by applying
spatial transformations, which creates adversarial examples by changing the
pixel locations independently to chrominance channels of perceptual colorspaces
such as $YC_{b}C_{r}$ and $CIELAB$, instead of making an additive perturbation
or manipulating pixel values directly. In a targeted white-box attack setting,
the proposed method is able to obtain competitive fooling rates with very high
confidence. The experimental evaluations show that the proposed method has
favorable results in terms of approximate perceptual distance between benign
and adversarially generated images. The source code is publicly available at
https://github.com/ayberkydn/stadv-torch
