With the advancement of generative AI, generative models such as Generative Adversarial Networks (GANs) and diffusion-based models have significantly enhanced the capabilities in generating photorealistic images. Despite significant progress in AI-Generated Images (AIGIs), the evaluation of AIGI quality remains underexplored in multiple aspects, including AI artifacts, unnatural content, and the availability of input text prompts. However, existing methods are insufficient to capture both feature representations and correlations between image and text for AIGI quality assessment in aspects of perceptual image quality and text-to-image alignment. To address this, we proposed a novel Prompt-Image-Caption Consistency (PICC) framework, designed to adapt a pre-trained vision-language model for AIGI quality assessment by considering both perceptual image quality and text-to-image alignment at the same time. This framework effectively explores the prompt-image-caption triplet by adapting image embedding and multimodal embedding via Quality-Aware Attention to capture quality-aware features and calculating consistency scores to build the correlations among the prompt, image, and caption. Additionally, we propose a multilevel strategy to integrate both local and global information from multiple prompt-image-caption triplets, further enhancing prediction performance. Extensive experiments on benchmark datasets, including AIGIQA-20K and AGIQA-3K, demonstrate that the proposed PICC achieves state-of-the-art performance compared to baseline methods. The code will be made publicly available.

Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image generation try to achieve attacks for most models, while few of them make efforts on guaranteeing the perceptual quality of the adversarial examples. High quality adversarial examples matter for many applications, especially for the privacy preserving. In this work, we develop a framework based on the Minimum Noticeable Difference (MND) concept to generate adversarial privacy preserving images that have minimum perceptual difference from the clean ones but are able to attack deep learning models. To achieve this, an adversarial loss is firstly proposed to make the deep learning models attacked by the adversarial images successfully. Then, a perceptual quality-preserving loss is developed by taking the magnitude of perturbation and perturbation-caused structural and gradient changes into account, which aims to preserve high perceptual quality for adversarial image generation. To the best of our knowledge, this is the first work on exploring quality-preserving adversarial image generation based on the MND concept for privacy preserving. To evaluate its performance in terms of perceptual quality, the deep models on image classification and face recognition are tested with the proposed method and several anchor methods in this work. Extensive experimental results demonstrate that the proposed MND framework is capable of generating adversarial images with remarkably improved performance metrics (e.g., PSNR, SSIM, and MOS) than that generated with the anchor methods.