The adage "Beautiful Outside But Ugly Inside" resonates with the security and explainability challenges encountered in image aesthetics assessment (IAA). Although deep neural networks (DNNs) have demonstrated remarkable performance in various IAA tasks, how to probe, explain, and enhance aesthetics-oriented "black-box" models has not yet been investigated to our knowledge. This lack of investigation has significantly impeded the commercial application of IAA. In this paper, we investigate the susceptibility of current IAA models to adversarial attacks and aim to elucidate the underlying mechanisms that contribute to their vulnerabilities. To address this, we propose a novel diffusion-based framework as an attacker (DA3Attacker), capable of generating adversarial examples (AEs) to deceive diverse black-box IAA models. DA3Attacker employs a dedicated Attack Diffusion Transformer, equipped with modular aesthetics-oriented filters. By undergoing two unsupervised training stages, it constructs a latent space to generate AEs and facilitates two distinct yet controllable attack modes: restricted and unrestricted. Extensive experiments on 26 baseline models demonstrate that our method effectively explores the vulnerabilities of these IAA models, while also providing multi-attribute explanations for their feature dependencies. To facilitate further research, we contribute the evaluation tools and four metrics for measuring adversarial robustness, as well as a dataset of 60,000 re-labeled AEs for fine-tuning IAA models. The resources are available here.
He et al. (Fri,) studied this question.