  Adversarial perturbations can be added to images to protect their content
from unwanted inferences. These perturbations may, however, be ineffective
against classifiers that were not {seen} during the generation of the
perturbation, or against defenses {based on re-quantization, median filtering
or JPEG compression. To address these limitations, we present an adversarial
attack {that is} specifically designed to protect visual content against {
unseen} classifiers and known defenses. We craft perturbations using an
iterative process that is based on the Fast Gradient Signed Method and {that}
randomly selects a classifier and a defense, at each iteration}. This
randomization prevents an undesirable overfitting to a specific classifier or
defense. We validate the proposed attack in both targeted and untargeted
settings on the private classes of the Places365-Standard dataset. Using
ResNet18, ResNet50, AlexNet and DenseNet161 {as classifiers}, the performance
of the proposed attack exceeds that of eleven state-of-the-art attacks. The
implementation is available at https://github.com/smartcameras/RP-FGSM/.
