  Backdoors and poisoning attacks are a major threat to the security of
machine-learning and vision systems. Often, however, these attacks leave
visible artifacts in the images that can be visually detected and weaken the
efficacy of the attacks. In this paper, we propose a novel strategy for hiding
backdoor and poisoning attacks. Our approach builds on a recent class of
attacks against image scaling. These attacks enable manipulating images such
that they change their content when scaled to a specific resolution. By
combining poisoning and image-scaling attacks, we can conceal the trigger of
backdoors as well as hide the overlays of clean-label poisoning. Furthermore,
we consider the detection of image-scaling attacks and derive an adaptive
attack. In an empirical evaluation, we demonstrate the effectiveness of our
strategy. First, we show that backdoors and poisoning work equally well when
combined with image-scaling attacks. Second, we demonstrate that current
detection defenses against image-scaling attacks are insufficient to uncover
our manipulations. Overall, our work provides a novel means for hiding traces
of manipulations, being applicable to different poisoning approaches.
