Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI (2406.12027v1)

Abstract: Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections -- with millions of downloads -- and show they only provide a false sense of security. We find that low-effort and "off-the-shelf" techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. Through a user study, we demonstrate that all existing protections can be easily bypassed, leaving artists vulnerable to style mimicry. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative non-technological solutions.

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

This paper, authored by Robert Hönig, Javier Rando, Nicholas Carlini, and Florian Tramèr, critically examines the viability of adversarial perturbations as a method to protect artists from style mimicry facilitated by generative AI. These authors analyze the efficacy of established protection tools such as Glaze, Mist, and Anti-DreamBooth in safeguarding artists' unique styles from being replicated by finetuned generative models.

Key Findings

The paper meticulously deconstructs the protections offered by Glaze, Mist, and Anti-DreamBooth, providing a comprehensive evaluation through various robust mimicry methods. Their investigation reveals several significant vulnerabilities and insights:

1. Brittleness of Protections: The authors show the inherent brittleness in the Glaze protection which is highly sensitive to variations in the finetuning process. Using an alternative, off-the-shelf finetuning script significantly degraded Glaze's efficacy, highlighting the non-generalizable nature of such adversarial perturbations.
2. Effectiveness of Robust Mimicry Methods: The paper introduces and evaluates multiple low-effort robust mimicry techniques including Gaussian noising, DiffPure, and Noisy Upscaling. Each method is analyzed for its capacity to circumvent protections, with findings indicating that even simple preprocessing methods diminish the protectiveness of the existing tools considerably.
3. Comprehensive Evaluation via User Study: Through a user paper composed of participants from Amazon Mechanical Turk (MTurk), the authors assess the success rates of these robust mimicry methods. Noisy Upscaling is identified as particularly effective, often generating images almost indistinguishable from those produced using unprotected images.

The authors conclude that all the evaluated protection methods—Glaze, Mist, and Anti-DreamBooth—fail to provide reliable security against motivated style forgers who employ these robust mimicry techniques. Their recommendations stress reevaluating these protections, owing to their significant intrinsic limitations.

Implications and Future Work

Theoretical Implications:

The findings draw a parallel to the broader adversarial machine learning landscape, where first-mover disadvantage plays a critical role. Adversarial perturbations, much like defenses against traditional adversarial attacks, face an inherent challenge: they can be adaptively circumvented, making their long-term reliability dubious.

Practical Implications:

Artists relying on these protections might face an undue false sense of security. The result could be detrimental, leading to more frequent unauthorized use of their styles as the protections do not hold up against adaptive adversaries.

Future Directions:

Future research should pivot towards alternative protective measures that are less susceptible to circumvention. They may include methods focusing on watermarking, legal frameworks providing rights and usage constraints, and potentially new technical efforts beyond the remit of adversarial perturbations that could provide more stable and effective protections.

Conclusion

The critique of current adversarial perturbation-based protections elucidated in this paper serves as a fundamental evaluation, presenting valuable insights for both researchers and practitioners. While the tested protections fail against even simple robustness interventions, the paper decisively encourages the exploration of new protective paradigms to ensure better preservation of artistic originality in the face of advancing generative AI capabilities.