{"type":"ScholarlyArticle","title":"Disrupting Diffusion-based Inpainters with Semantic Digression","authors":[{"name":"Geonho Son","slug":"geonho-son","given":"Geonho","family":"Son","affiliation":"Department of Artificial Intelligence"},{"name":"Juhun Lee","slug":"juhun-lee","given":"Juhun","family":"Lee","affiliation":"Department of Artificial Intelligence"},{"name":"Simon S. Woo","slug":"simon-s-woo","given":"Simon S.","family":"Woo","affiliation":"Department of Artificial Intelligence; Computer Science & Engineering Department Sungkyunkwan University"}],"abstract":"The fabrication of visual misinformation on the web and social media has increased exponentially with the advent of foundational text-to-image diffusion models. Namely, Stable Diffusion inpainters allow the synthesis of maliciously inpainted images of personal and private figures, and copyrighted contents, also known as deepfakes. To combat such generations, a disruption framework, namely Photoguard, has been proposed, where it adds adversarial noise to the context image to disrupt their inpainting synthesis. While their framework suggested a diffusion-friendly approach, the disruption is not sufficiently strong and it requires a significant amount of GPU and time to immunize the context image. In our work, we re-examine both the minimal and favorable conditions for a successful inpainting disruption, proposing DDD, a “Digression guided Diffusion Disruption” framework. First, we identify the most adversarially vulnerable diffusion timestep range with respect to the hidden space. Within this scope of noised manifold, we pose the problem as a semantic digression optimization. We maximize the distance between the inpainting instance's hidden states and a semantic-aware hidden state centroid, calibrated both by Monte Carlo sampling of hidden states and a discretely projected optimization in the token space. Effectively, our approach achieves stronger disruption and a higher success rate than Photoguard while lowering the GPU memory requirement, and speeding the optimization up to three times faster.","doi":"10.24963/ijcai.2024/856","url":"https://ijcai.org/proceedings/2024/856","pdf":"https://ijcai.org/proceedings/2024/0856.pdf","markdown":"https://ijcai.org/proceedings/2024/856.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/856","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":856,"pages":{"first":7735,"last":7743},"keywords":["Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Other domains of art or creativity","Theory and philosophy of arts and creativity in AI systems: Cultural and social impacts of AI on creativity, creative practice, education and society","Theory and philosophy of arts and creativity in AI systems: Ethical issues raised by creative AI systems"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2023] Yida Chen, Fernanda Viégas, and Mar- tin Wattenberg. 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