Artwork protection against unauthorized neural style transfer and aesthetic color distance metric

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.