GLEAN: Generative Learning for Eliminating Adversarial Noise
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914950562185216 |
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| author | Kim, Justin Lyu Woo, Kyoungwan |
| author_facet | Kim, Justin Lyu Woo, Kyoungwan |
| contents | In the age of powerful diffusion models such as DALL-E and Stable Diffusion, many in the digital art community have suffered style mimicry attacks due to fine-tuning these models on their works. The ability to mimic an artist's style via text-to-image diffusion models raises serious ethical issues, especially without explicit consent. Glaze, a tool that applies various ranges of perturbations to digital art, has shown significant success in preventing style mimicry attacks, at the cost of artifacts ranging from imperceptible noise to severe quality degradation. The release of Glaze has sparked further discussions regarding the effectiveness of similar protection methods. In this paper, we propose GLEAN- applying I2I generative networks to strip perturbations from Glazed images, evaluating the performance of style mimicry attacks before and after GLEAN on the results of Glaze. GLEAN aims to support and enhance Glaze by highlighting its limitations and encouraging further development. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_10578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GLEAN: Generative Learning for Eliminating Adversarial Noise Kim, Justin Lyu Woo, Kyoungwan Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning In the age of powerful diffusion models such as DALL-E and Stable Diffusion, many in the digital art community have suffered style mimicry attacks due to fine-tuning these models on their works. The ability to mimic an artist's style via text-to-image diffusion models raises serious ethical issues, especially without explicit consent. Glaze, a tool that applies various ranges of perturbations to digital art, has shown significant success in preventing style mimicry attacks, at the cost of artifacts ranging from imperceptible noise to severe quality degradation. The release of Glaze has sparked further discussions regarding the effectiveness of similar protection methods. In this paper, we propose GLEAN- applying I2I generative networks to strip perturbations from Glazed images, evaluating the performance of style mimicry attacks before and after GLEAN on the results of Glaze. GLEAN aims to support and enhance Glaze by highlighting its limitations and encouraging further development. |
| title | GLEAN: Generative Learning for Eliminating Adversarial Noise |
| topic | Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.10578 |