Arbitrary style transfer (AST), a popular AI-powered photo editing function, aims to strike an optimal balance between content and style injection from two images in order to generate a novel high-fidelity stylised image. Recently, diffusion models have been applied to AST due to their high generation quality as well as flexibility to embed conditions. However, these models are still not satisfactory and may exhibit inferior performance compared to non-diffusion based methods. This is due to the diffusion process not being purposely designed for AST, leading to suboptimal solutions to trade-off content preservation and style embedding. In this paper, we propose ACID-Style, a novel adaptive condition injection diffusion-based AST framework for improved content/style feature injection to address this research challenge. Using two lightweight adapters, a content and a style injection module, and an adaptive injection mechanism, our approach is able to fully exploit a pre-trained stable diffusion model for AST-specific adaptation and our diffusion model thus learns the most effective timing for content and style injection in the diffusion sampling process. Comprehensive evaluations demonstrate that our method achieves superior style transfer performance, both quantitatively and qualitatively, compared to other state-of-the-art style transfer methods.

Adversarial attack has posed a significant threat to modern deep learning based models. Recently, various adversarial defending algorithms are proposed to tackle the problem. Among them, diffusion-based adversarial purification approaches offer the most promising solutions. However, their effectiveness are limited due to the strong adversarial perturbations presented in attacked images. These adversarial signals hinder the introduction of guidance into diffusion models in order to improve the defence efficacy. In this paper, we propose a novel approach to embed reliable guidance into diffusion-based adversarial purification model to improve both its defence effectiveness and efficiency. In specific, we present a diffusion sampling guidance enhanced by a pretrained denoising network as a prior constraint to improve the adversarial defence performance. Experimental results convincingly demonstrate the superior performance of the proposed approach in terms of enhanced robustness to standard image classifiers when compared to state-of-the-art adversarial defence approaches.

Abstract Face swapping is a technique that replaces a face in a target media with another face of a different identity from a source face image. Currently, research on the effective utilisation of prior knowledge and semantic guidance for photo‐realistic face swapping remains limited, despite the impressive synthesis quality achieved by recent generative models. In this paper, we propose a novel conditional Denoising Diffusion Probabilistic Model (DDPM) enforced by a two‐level face prior guidance. Specifically, it includes (i) an image‐level condition generated by a 3D Morphable Model (3DMM), and (ii) a high‐semantic level guidance driven by information extracted from several pre‐trained attribute classifiers, for high‐quality face image synthesis. Although swapped face image from 3DMM does not achieve photo‐realistic quality on its own, it provides a strong image‐level prior, in parallel with high‐level face semantics, to guide the DDPM for high fidelity image generation. The experimental results demonstrate that our method outperforms state‐of‐the‐art face swapping methods on benchmark datasets in terms of its synthesis quality, and capability to preserve the target face attributes and swap the source face identity.