Data protection in generative AI images

Abstract

Generative AI technologies, particularly text-to-image models, have revolutionized the creative domain by enabling the generation of high-quality images from textual prompts. Despite their transformative potential, these advancements pose critical challenges in the realms of copyright infringement, unauthorized style replication, and the preservation of artistic integrity. This research explores novel methodologies aimed at safeguarding artistic content through the development of advanced watermarking mechanisms and frameworks to deter unau- thorized style transfer. Traditional watermarking methods often struggle to maintain security while preserving visual quality. To overcome this, we leverage cutting-edge neural network architectures and incorporate perceptual and contrastive loss functions to design an imperceptible embedding framework. Adopting the low-frequency do- main loss to constrain the model to insert the secret information into the high-frequency area of the image, can significantly improve the security of the hiding process and resist various adversarial attacks while maintaining high visual fidelity. To evaluate the robustness and effectiveness of the proposed solution, state-of-the-art generative models, including DreamBooth and Textual Inversion, are employed to simulate scenarios of unauthorized use and fine-tuning. Experimental results reveal that the framework significantly outperforms traditional methods in preserving artistic ownership and preventing unauthorized modifications. This work overcomes a critical gap in the domain of style copyright protection. It serves as a crucial safeguard against unauthorized style transfer attempts by malicious plagiarizers, thereby significantly enhancing the security of original styles.