arXiv:2403.12053v1 Announce Type: new
Abstract: Integrating watermarks into generative images is a critical strategy for protecting intellectual property and enhancing artificial intelligence security. This paper proposes Plug-in Generative Watermarking (PiGW) as a general framework for integrating watermarks into generative images. More specifically, PiGW embeds watermark information into the initial noise using a learnable watermark embedding network and an adaptive frequency spectrum mask. Furthermore, it optimizes training costs by gradually increasing timesteps. Extensive experiments demonstrate that PiGW enables embedding watermarks into the generated image with negligible quality loss while achieving true invisibility and high resistance to noise attacks. Moreover, PiGW can serve as a plugin for various commonly used generative structures and multimodal generative content types. Finally, we demonstrate how PiGW can also be utilized for detecting generated images, contributing to the promotion of secure AI development. The project code will be made available on GitHub.
Integrating Watermarks into Generative Images: Enhancing AI Security
In the field of multimedia information systems, the protection of intellectual property and enhancing artificial intelligence security are two crucial areas of concern. This paper introduces a new approach called Plug-in Generative Watermarking (PiGW) that tackles these issues by offering a general framework for integrating watermarks into generative images.
PiGW utilizes a learnable watermark embedding network and an adaptive frequency spectrum mask to embed watermark information into the initial noise of the generative image. This technique ensures that the watermark remains hidden and resistant to noise attacks while causing negligible quality loss to the generated image. By gradually increasing timesteps during training, PiGW optimizes the training costs.
One of the significant advantages of PiGW is its versatility. It can be easily integrated as a plugin for various commonly used generative structures and multimodal generative content types. This multi-disciplinary aspect allows PiGW to be applied to different domains, ranging from animations to artificial reality, augmented reality, and virtual realities.
Regarding its relation to multimedia information systems, PiGW offers a novel solution for protecting intellectual property in the context of generative images. By integrating watermarks, it ensures that unauthorized copying or distribution of generative content can be traced back to its source, reducing the risk of infringement and promoting a fair environment for creators and developers.
In the wider field of animations, PiGW opens up new possibilities for secure distribution and copyright protection. Watermarked generative images can be used to create unique animations that are resistant to tampering or unauthorized modifications, preserving the original creator’s vision and rights.
Furthermore, in the domains of artificial reality, augmented reality, and virtual realities, PiGW plays a crucial role in maintaining the integrity and authenticity of generated content. With the rapid advancement of technologies in these fields, there is an increasing need for secure methods of verifying the origin and ownership of generative content. PiGW’s ability to embed watermarks invisibly and resist noise attacks contributes to the overall security of these systems.
Lastly, PiGW also contributes to the development of secure AI by offering a means to detect generated images. This capability helps in distinguishing between real and generated content and mitigates the risk of malicious use or misinformation through the creation of misleading images. By providing the project code on GitHub, the authors foster transparency and collaboration in the AI community, encouraging the adoption and further development of PiGW.
In conclusion, Plug-in Generative Watermarking (PiGW) brings together concepts from various disciplines, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Its integration of watermarks into generative images offers a robust solution for intellectual property protection and enhances the security of artificial intelligence. As the field continues to evolve, it is expected that PiGW will find applications in a diverse range of domains, playing a crucial role in securing and authenticating generative content.