Object-oriented backdoor attack against image captioning
Meiling Li Nan Zhong Xinpeng Zhang Zhenxing Qian Sheng Li
Backdoor attack against image classification task has been widely studied and
proven to be successful, while there exist little research on the backdoor
attack against vision-language models. In this paper, we explore backdoor
attack towards image captioning models by poisoning training data. Assuming the
attacker has total access to the training dataset, and cannot intervene in
model construction or training process. Specifically, a portion of benign
training samples is randomly selected to be poisoned. Afterwards, considering
that the captions are usually unfolded around objects in an image, we design an
object-oriented method to craft poisons, which aims to modify pixel values by a
slight range with the modification number proportional to the scale of the
current detected object region. After training with the poisoned data, the
attacked model behaves normally on benign images, but for poisoned images, the
model will generate some sentences irrelevant to the given image. The attack
controls the model behavior on specific test images without sacrificing the
generation performance on benign test images. Our method proves the weakness of
image captioning models to backdoor attack and we hope this work can raise the
awareness of defending against backdoor attack in the image captioning field.