Stealthy Targeted Backdoor Attack Against Image Captioning

Wenshu Fan, Hongwei Li, Wenbo Jiang, Meng Hao, Xiao Zhang

Keywords: Machine Learning, Image Caption, Backdoor Attack

Abstract: We study backdoor attacks against image caption models, whose security issues have received less scrutiny compared with other multimodal tasks. Existing backdoor attacks typically pair a trigger either with a predefined sentence or a single word as the targeted output, yet they are unrelated to the image content, making them easily noticeable as anomalies by humans. In this paper, we present a novel method to craft targeted backdoor attacks against image caption models, which are designed to be stealthier than prior attacks. Specifically, our method first learns a special trigger by leveraging universal perturbation techniques for object detection, then places the learned trigger in the center of some specific source object and modifies the corresponding object name in the output caption to a predefined target name. During the prediction phase, the caption produced by the backdoored model for input images with the trigger can accurately convey the semantic information of the rest of the whole image, while incorrectly recognizing the source object as the predefined target. Extensive experiments demonstrate that our approach can achieve a high attack success rate while having a negligible impact on model clean performance. In addition, we show our method is stealthy in that the produced backdoor samples are indistinguishable from clean samples in both image and text domains, which can successfully bypass existing backdoor defenses, highlighting the need for better defensive mechanisms against such stealthy backdoor attacks.

Primary Area: societal considerations including fairness, safety, privacy

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Submission Number: 3390