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With rich visual data, such as images, becoming readily associated with items, visually-aware recommendation systems (VARS) have been widely used in different applications. Recent studies have shown that VARS are vulnerable to item-image adversarial attacks, which add human-imperceptible perturbations to the clean images associated with those items. Attacks on VARS pose new security challenges to a wide range of applications, such as e-commerce and social media, where VARS are widely used. How to secure VARS from such adversarial attacks becomes a critical problem. Currently, there is still a lack of systematic studies on how to design defense strategies against visual attacks on VARS. In this article, we attempt to fill this gap by proposing anadversarial image denoising and detectionframework to secure VARS. Our proposed method can simultaneously (1) secure VARS from adversarial attacks characterized bylocalperturbations by image denoising based onglobalvision transformers; and (2) accurately detect adversarial examples using a novel contrastive learning approach. Meanwhile, our framework is designed to be used as both a filter and a detector so that they can bejointlytrained to improve the flexibility of our defense strategy to a variety of attacks and VARS models. Our approach is uniquely tailored for VARS, addressing the distinct challenges in scenarios where adversarial attacks can differ across industries, for instance, causing misclassification in e-commerce or misrepresentation in real estate. We have conducted extensive experimental studies with two popular attack methods (FGSM and PGD). Our experimental results on two real-world datasets show that our defense strategy against visual attacks is effective and outperforms existing methods on different attacks. Moreover, our method demonstrates high accuracy in detecting adversarial examples, complementing its robustness across various types of adversarial attacks. 

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.