CAPatch: Physical Adversarial Patch against Image Captioning Systems

The fast-growing surveillance systems will make image captioning, i.e., automatically generating text descriptions of images, an essential technique to process the huge volumes of videos efficiently, and correct captioning is essential to ensure the text authenticity. While prior work has demonstrated the feasibility of fooling computer vision models with adversarial patches, it is unclear whether the vulnerability can lead to incorrect captioning, which involves natural language processing after image feature extraction. In this paper, we design CAPatch, a physical adversarial patch that can result in mistakes in the final captions, i.e., either create a completely different sentence or a sentence with keywords missing, against multi-modal image captioning systems. To make CAPatch effective and practical in the physical world, we propose a detection assurance and attention enhancement method to increase the impact of CAPatch and a robustness improvement method to address the patch distortions caused by image printing and capturing. Evaluations on three commonly-used image captioning systems (Show-and-Tell, Self-critical Sequence Training: Att2in, and Bottom-up Top-down) demonstrate the effectiveness of CAPatch in both the digital and physical worlds, whereby volunteers wear printed patches in various scenarios, clothes, lighting conditions. With a size of 5% of the image, physically-printed CAPatch can achieve continuous attacks with an attack success rate higher than 73.1% over a video recorder.