Hosseini and Poovendran propose semantic adversarial examples by randomly manipulating hue and saturation of images. In particular, in an iterative algorithm, hue and saturation are randomly perturbed and projected back to their valid range. If this results in mis-classification the perturbed image is returned as the adversarial example and the algorithm is finished; if not, another iteration is run. The result is shown in Figure 1. As can be seen, the structure of the images is retained while hue and saturation changes, resulting in mis-classified images.
Figure 1: Examples of the computed semantic adversarial examples.
Hosseini and Poovendran propose semantic adversarial examples by randomly manipulating hue and saturation of images. In particular, in an iterative algorithm, hue and saturation are randomly perturbed and projected back to their valid range. If this results in mis-classification the perturbed image is returned as the adversarial example and the algorithm is finished; if not, another iteration is run. The result is shown in Figure 1. As can be seen, the structure of the images is retained while hue and saturation changes, resulting in mis-classified images.
Figure 1: Examples of the computed semantic adversarial examples.