With state-of-the-art performance in image synthesis especially in text-to-image generation, diffusion models (DMs) have received unprecedented attention. Despite promising application prospects, fake images generated from diffusion models are causing potential security concerns. To this end, in this work, we aim to investigate whether the generated images of DMs are different from other generated models e.g. generative adversarial networks (GANs) and whether a universal forensic classifier exists. To perform this work, we first collected a dataset consisting of 409k fake images generated from different types of DMs. Through a comprehensive analysis on this benchmark, we showcased that common forensic artifacts are shared among DMs and a forensic classifier trained for one model can generalize well for other agnostic generative models. Specifically, we first demonstrated that despite photorealism of the images generated by DMs, they still contain artifacts namely non-robust visual features which are hard for human but easy for machine to recognize. Then we studied the characteristics of the artifacts from the view of adversarial attack and unexpectedly found there exists a universal adversarial perturbation to fool the classifier. Furthermore, we devised visualization and analysis tools focusing on the spectral properties of the generated samples and adversarial features which demonstrates augmentations in the frequency domain greatly affect the performance of the detectors.

Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on the reference-based fashion design task, where we aim to combine a reference appearance image and a clothing image to generate a new fashion clothing image. Although existing diffusion-based image translation methods have enabled flexible style transfer, it is often difficult to transfer the appearance of the image realistically during reverse diffusion. When the referenced appearance domain greatly differs from the source domain, it often leads to the collapse in the translation. To tackle this issue, we present a novel diffusion model-based unsupervised structure-aware transfer method, namelyDiffFashion. Our method is free of model tuning and structure-preserving and has high flexibility in transferring from images with large domain gaps. Specifically, based on the optimal transport properties, we keep a shared latent across the clothing image and reference appearance image to bridge the gap between the two domains in the denoising process, and the latent of the reference image is gradually adapted to the clothing domain. Simultaneously, the structure is transferred from the source clothing to the output fashion image with mixed guidance, including pre-trained Vision Transformer (ViT) guidance and a foreground mask guidance, to further preserve the structure and appearance semantics from source and reference images. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task.

Observation is common for children to connect with nature, increasing their knowledge and awareness of biodiversity. However, it is challenging for them to make and document their observations due to a lack of observation and drawing skills. Therefore, we designed an interactive drawing tool, Bio Sketchbook, which scaffolds children in systematic observation, observational drawing, and knowledge acquisition. It can recognize plant species and generate contour drawings from children’s photographs, guiding them to observe and draw multi-dimensional plant features with a digital magnifier and in-context biological information. Our in-situ user study with 19 children revealed that Bio Sketchbook provided an engaging experience and effectively supported children in recording and retaining biodiversity information and in balancing observations with screen time. Additionally, Bio Sketchbook intervened in children’s interaction with plants by prompting observational behaviors, encouraging them to directly touch and establish rapport with plants, and arousing their interest and knowledge of plants.

Laser cutting revolutionizes the creation of personal-fabricated prototypes. These objects can have transformable properties by adopting different materials and be interactive by integrating electronic circuits. However, circuits in laser-cut objects always have limited movements, which refrains laser cutting from achieving interactive prototypes with more complex movable functions like mechanisms. We propose MechCircuit, a design and fabrication pipeline for making mechanical-electronical objects with laser cutting. We leverage the neodymium magnet’s natures of magnetism and conductivity to integrate electronics and mechanical structure joints into prototypes. We conduct the evaluation to explore technological parameters and assess the practical feasibility of the fabrication pipeline. And we organized a user-observing workshop for non-expert users. Through the outcoming prototypes, the result demonstrates the feasibility of MechCircuit as a useful and inspiring prototyping method.