The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from being identified by unauthorized FR systems utilizing adversarial attacks to generate encrypted face images. However, existing methods suffer from poor visual quality or low attack success rates, which limit their utility. Recently, diffusion models have achieved tremendous success in image generation. In this work, we ask: can diffusion models be used to generate adversarial examples to improve both visual quality and attack performance? We propose DiffProtect, which utilizes a diffusion autoencoder to generate semantically meaningful perturbations on FR systems. Extensive experiments demonstrate that DiffProtect produces more natural-looking encrypted images than state-of-the-art methods while achieving significantly higher attack success rates, e.g., 24.5% and 25.1% absolute improvements on the CelebA-HQ and FFHQ datasets.
Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists' rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), enabling artists to conveniently protect their work from unauthorized NST by pre-processing the artwork image before public release, providing content-independent protection regardless of which content image it may later be combined with. LAACA introduces adaptive perturbations that significantly degrade NST quality while maintaining the visual integrity of the original image. We also develope LAACAv2, which resists the current SOTA adversarial perturbation removal method - SDEdit-based adversarial purification. Additionally, we introduce the Aesthetic Color Distance Metric (ACDM) to better evaluate color-sensitive tasks like NST. Extensive experiments across various NST techniques demonstrate our methods outperform baselines in structural similarity, color preservation, and perceptual quality. User studies with both general users and art experts confirm the practical applicability of our approach, addressing the social trust crisis in the art community while advancing adversarial machine learning at the intersection of art, technology, and intellectual property rights.
Our purpose in this paper is twofold. (a) We discuss the computational problem of deciding whether a given graph is the commuting graph of a Ti-nite group; we give a quasipolynomial algorithm, and a polynomial algorithm for the case when the group is an extraspecial p-group for pan odd prime. (b) We give new results on the question of whether the com-muting graph of a given group is a cograph or a chordal graph, two classes of graphs defined by forbidden sub-graphs. The problems are not unrelated, since there are a number of cases where hard computational problems on graphs are eas-ier when restricted to special classes of graphs; we conjecture that the recognition problem is polynomial for cographs and chordal graphs. @2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)-
Over the past few decades, various antifogging strategies and preparation methods have been proposed. Unfortunately, a surface with a single antifogging function cannot achieve a wide range of practical applications. For example, medical endoscopes require antifogging and antibacterial capabilities to improve diagnostic accuracy and safety. Inspired by the near-perfect multifunctional properties of natural creatures, antifogging materials with specific functions have drawn more and more attention owing to their promising and wide applications. However, the design of bioinspired antifogging surfaces with broad applicability still presents some challenges, such as the integration of multifunctional properties, and the optimization of preparation routes. In this review, beginning with the fogging mechanism and wettability theory, the latest antifogging surface materials and pattern designs are analyzed in detail and critically evaluated. The natural biomaterials with multifunctional characteristics are summarized, and the integration mechanism and design difficulties of the four multifunctional characteristics are then emphatically analyzed. Based on artificial intelligence (AI) assisted design optimization, we introduce the neural network into the bionic multifunction antifogging path realization for the first time and summarize the antifogging prototype and antifogging multifunction database. Finally, the challenges and future trends of bioinspired multifunction antifogging surfaces (MF-AFS) are presented.
This study is concerned with how founding stories are sustained across multiple generations of employees in family firms and how these stories influence organizational identification. Drawing on a social memory perspective and narrative memory work, we explore the retold founding stories of employees in a large agricultural family firm. Our study demonstrates that founding stories transform firsthand memories into collective memory across multiple generations through intertwining intradiegetic storytelling with material and relational processes. The effortful work of remembering together across familial and social relations, spaces, and embodied ways explains how successive generations understand their belongingness to the organization.
