  Visually-aware recommender systems have found widespread application in
domains where visual elements significantly contribute to the inference of
users' potential preferences. While the incorporation of visual information
holds the promise of enhancing recommendation accuracy and alleviating the
cold-start problem, it is essential to point out that the inclusion of item
images may introduce substantial security challenges. Some existing works have
shown that the item provider can manipulate item exposure rates to its
advantage by constructing adversarial images. However, these works cannot
reveal the real vulnerability of visually-aware recommender systems because (1)
The generated adversarial images are markedly distorted, rendering them easily
detectable by human observers; (2) The effectiveness of the attacks is
inconsistent and even ineffective in some scenarios. To shed light on the real
vulnerabilities of visually-aware recommender systems when confronted with
adversarial images, this paper introduces a novel attack method, IPDGI (Item
Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided
diffusion model to generate adversarial samples designed to deceive
visually-aware recommender systems. Taking advantage of accurately modeling
benign images' distribution by diffusion models, the generated adversarial
images have high fidelity with original images, ensuring the stealth of our
IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct
extensive experiments on two commonly used e-commerce recommendation datasets
(Amazon Beauty and Amazon Baby) with several typical visually-aware recommender
systems. The experimental results show that our attack method has a significant
improvement in both the performance of promoting the long-tailed (i.e.,
unpopular) items and the quality of generated adversarial images.
