  Co-salient object detection (CoSOD) has recently achieved significant
progress and played a key role in retrieval-related tasks. However, it
inevitably poses an entirely new safety and security issue, i.e., highly
personal and sensitive content can potentially be extracting by powerful CoSOD
methods. In this paper, we address this problem from the perspective of
adversarial attacks and identify a novel task: adversarial co-saliency attack.
Specially, given an image selected from a group of images containing some
common and salient objects, we aim to generate an adversarial version that can
mislead CoSOD methods to predict incorrect co-salient regions. Note that,
compared with general white-box adversarial attacks for classification, this
new task faces two additional challenges: (1) low success rate due to the
diverse appearance of images in the group; (2) low transferability across CoSOD
methods due to the considerable difference between CoSOD pipelines. To address
these challenges, we propose the very first black-box joint adversarial
exposure and noise attack (Jadena), where we jointly and locally tune the
exposure and additive perturbations of the image according to a newly designed
high-feature-level contrast-sensitive loss function. Our method, without any
information on the state-of-the-art CoSOD methods, leads to significant
performance degradation on various co-saliency detection datasets and makes the
co-salient objects undetectable. This can have strong practical benefits in
properly securing the large number of personal photos currently shared on the
Internet. Moreover, our method is potential to be utilized as a metric for
evaluating the robustness of CoSOD methods.
