  The eye fixation patterns of human observers are a fundamental indicator of
the aspects of an image to which humans attend. Thus, manipulating fixation
patterns to guide human attention is an exciting challenge in digital image
processing. Here, we present a new model for manipulating images to change the
distribution of human fixations in a controlled fashion. We use the
state-of-the-art model for fixation prediction to train a convolutional neural
network to transform images so that they satisfy a given fixation distribution.
For network training, we carefully design a loss function to achieve a
perceptual effect while preserving naturalness of the transformed images.
Finally, we evaluate the success of our model by measuring human fixations for
a set of manipulated images. On our test images we can in-/decrease the
probability to fixate on selected objects on average by 43/22% but show that
the effectiveness of the model depends on the semantic content of the
manipulated images.
