Abstract
Adversarial attacks are known to succeed on classifiers,
but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which
incorporate natural language understanding and complex
structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense
captioning model and on two visual question answering
(VQA) models. Our evaluation shows that we can generate
adversarial examples with a high success rate (i.e., > 90%)
for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box
localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.