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The harm caused by deepfake face images is increasing. To proactively defend against this threat, this paper innovatively proposes a destructive active defense algorithm for deepfake face images (DADFI). This algorithm adds slight perturbations to the original face images to generate adversarial samples. These perturbations are imperceptible to the human eye but cause significant distortions in the outputs of mainstream deepfake models. Firstly, the algorithm generates adversarial samples that maintain high visual fidelity and authenticity. Secondly, in a black-box scenario, the adversarial samples are used to attack deepfake models to enhance their offensive capabilities. Finally, destructive attack experiments were conducted on the mainstream face datasets CASIA-FaceV5 and CelebA. The results demonstrate that the proposed DADFI algorithm not only improves the generation speed of adversarial samples but also increases the success rate of active defense. This achievement can effectively reduce the harm caused by deepfake face images. [ABSTRACT FROM AUTHOR]

Most image quality assessment (IQA) algorithms based on sparse representation primarily focus on amplitude information, often overlooking the structural composition of images. However, structural composition is closely linked to perceived image quality, a connection that existing methods do not adequately address. To fill this gap, this paper proposes a novel distorted-image quality assessment algorithm based on a sparse structure and subjective perception (IQA-SSSP). This algorithm evaluates the quality of distorted images by measuring the sparse structure similarity between a reference and distorted images. The proposed method has several advantages. First, the sparse structure algorithm operates with reduced computational complexity, leading to faster processing speeds, which makes it suitable for practical applications. Additionally, it efficiently handles large-scale data, further enhancing the assessment process. Experimental results validate the effectiveness of the algorithm, showing that it achieves a high correlation with human visual perception, as reflected in both objective and subjective evaluations. Specifically, the algorithm yielded a Pearson correlation coefficient of 0.929 and a mean squared error of 8.003, demonstrating its robustness and efficiency. By addressing the limitations of existing IQA methods and introducing a more holistic approach, this paper offers new perspectives on IQA. The proposed algorithm not only provides reliable quality assessment results but also closely aligns with human visual experience, thereby enhancing both the objectivity and accuracy of image quality evaluations. This research offers significant theoretical support for the advancement of sparse representation in IQA. [ABSTRACT FROM AUTHOR]

Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is based on the sensitivity of contour occlusion and a binocular stereoscopic vision device. In this method, a depth contour map is combined with a sparse depth map obtained from a two-stage adaptive filter area stereo matching algorithm and the depth contour map of the objects extracted by a digital image stabilisation optical flow method. We also propose a quadratic optimisation model with three constraints to generate an accurate dense map of the depth contour for high-quality real-virtual occlusion. The whole process is accelerated by GPU. To evaluate the effectiveness of the algorithm, we demonstrate a time consumption statistical analysis for each stage of the DCO algorithm execution. To verify the reliability of the real-virtual occlusion effect, we conduct an experimental analysis on single-sided, enclosed, and complex occlusions. Subsequently, we compare it with the occlusion method without quadratic optimisation. With our GPU implementation for real-time DCO, the evaluation indicates that applying the presented DCO algorithm enhances the real-time performance and the visual quality of real-virtual occlusion. [ABSTRACT FROM AUTHOR]