The paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" aims to address the issue of illegal adaptation of diffusion models by introducing a technique called FreezeAsGuard, which involves freezing model tensors critical for illegal domains to prevent unauthorized adaptation . This problem is not entirely new, as existing methods focus on modifying training data or model weights, which can be easily reversed by users through fine-tuning with custom data . However, FreezeAsGuard presents a novel approach by selectively freezing tensors in pre-trained models to limit the model's representation power in illegal domains during fine-tuning, thereby preventing the relearning of illegal knowledge .

This paper aims to validate the scientific hypothesis that better pre-trained models with more modularized knowledge distribution over model parameters allow FreezeAsGuard to freeze them without affecting innocent domains, resulting in improved performance in mitigating illegal adaptation of diffusion models . The hypothesis suggests that by selectively freezing critical tensors in pre-trained models, the convergence of fine-tuning in illegal domains can be limited, thereby preventing the relearning of unlearned knowledge in fine-tuning and enhancing the model's representation power in illegal domains .

The paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" introduces a novel technique called FreezeAsGuard, which aims to address the issue of illegal adaptation of diffusion models by selectively freezing model tensors that are critical for adaptation in illegal domains . This technique outperforms existing model unlearning schemes and is designed to be applicable to various large generative models .

One key aspect of FreezeAsGuard is its ability to retain the representation power of the model when fine-tuned in innocent domains by freezing only the adaptation-critical tensors for illegal domains . By incorporating training samples from innocent domains into the optimization process, FreezeAsGuard ensures that the frozen tensors do not impact fine-tuning in innocent domains, leading to minimal impact on legal model adaptation .

The paper evaluates FreezeAsGuard in the context of generating fake portraits of public figures using open-sourced diffusion models. It demonstrates that FreezeAsGuard has strong mitigation power in illegal domains, reducing the quality of generated images compared to baselines and ensuring that the generated images are unrecognizable as target subjects . Additionally, FreezeAsGuard shows high compute efficiency, saving GPU memory and wall-clock time during model fine-tuning for innocent users .

Furthermore, FreezeAsGuard's mitigation power is selective and focuses specifically on subjects' faces in the generated images. This selectivity allows FreezeAsGuard to outperform other unlearning methods like UCE and IMMA, which may not effectively prevent relearning of illegal domain knowledge during fine-tuning . The paper also discusses how freezing more model tensors can potentially reduce overfitting in illegal domains and improve the quality of fine-tuning, showcasing the versatility and effectiveness of FreezeAsGuard in different scenarios . The FreezeAsGuard technique proposed in the paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" offers several key characteristics and advantages compared to previous methods .

1. Selective Tensor Freezing: FreezeAsGuard selectively freezes model tensors that are critical for adaptation in illegal domains, allowing the model to retain its representation power when fine-tuned in innocent domains . This selective freezing approach ensures that the frozen tensors do not impact fine-tuning in innocent domains, minimizing the impact on legal model adaptation .

2. Mitigation Power: FreezeAsGuard demonstrates strong mitigation power in illegal domains, reducing the quality of images generated by fine-tuned models by up to 14% compared to baselines. It ensures that the generated images are unrecognizable as target subjects, enhancing privacy protection .

3. Minimal Impact on Legal Model Adaptation: In innocent domains, FreezeAsGuard has minimal impact on legal model adaptation. It can achieve comparable image quality to regular full fine-tuning on innocent datasets and even improve accuracy by up to 8% compared to competitive baselines .

4. Compute Efficiency: FreezeAsGuard offers high compute efficiency, saving up to 48% of GPU memory and 21% of wall-clock time during model fine-tuning for innocent users. This efficiency makes the technique practical and resource-friendly for real-world applications .

5. Versatility and Effectiveness: FreezeAsGuard's mitigation power is selective and effective, focusing specifically on critical model components for illegal domain adaptation. By freezing tensors strategically, FreezeAsGuard outperforms other unlearning methods and can potentially reduce overfitting in illegal domains, improving the quality of fine-tuning in various scenarios .

Overall, FreezeAsGuard's innovative approach to selective tensor freezing, coupled with its strong mitigation power, minimal impact on legal model adaptation, compute efficiency, and versatility, positions it as a promising technique for addressing the challenges of illegal adaptation of diffusion models .

Several related researches exist in the field of mitigating illegal adaptation of diffusion models. One notable paper on this topic is "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" . The key solution proposed in this paper is the concept of FreezeAsGuard, which advocates selectively freezing critical tensors in pre-trained models to limit the model's representation power during fine-tuning in illegal domains . This approach aims to prevent users from easily reversing mitigation maneuvers applied to diffusion models and ensures that the model's tensors are constrained in a way that prevents illegal adaptation without affecting innocent domains .

Noteworthy researchers in this field include the authors of the paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" . They have introduced a novel technique that addresses the challenge of preventing illegal adaptation of diffusion models by selectively freezing tensors critical to fine-tuning in illegal domains . This innovative approach marks a fundamental shift in constraining the trainability of diffusion model's tensors to enhance security and prevent unauthorized data use .

The experiments in the paper were designed to evaluate the effectiveness of FreezeAsGuard in mitigating illegal model adaptation by generating fake portraits of public figures using open-sourced SD models. The experiments involved the following key components and methodologies:

• Dataset Selection: The experiments utilized a self-collected dataset of 25 public figures' portraits as the illegal domain, while the Modern-Logo-v4 and H&M-Clothes datasets were used as innocent domains .
• Baseline Comparison: Competitive model unlearning schemes were used as baselines for comparison with FreezeAsGuard .
• Evaluation Metrics: The quality of images generated by the fine-tuned model was assessed, and the impact on legal model adaptation in innocent domains was measured .
• Mitigation Power: FreezeAsGuard demonstrated strong mitigation power in illegal domains, reducing the quality of generated images by 14% compared to baselines. It ensured that the generated images were unrecognizable as target subjects .
• Impact on Legal Model Adaptation: FreezeAsGuard had minimal impact on legal model adaptation in innocent domains, achieving comparable image quality with regular full fine-tuning on innocent datasets. It even improved accuracy by up to 8% compared to competitive baselines .
• Compute Efficiency: FreezeAsGuard exhibited high compute efficiency, saving up to 48% GPU memory and 21% wall-clock time during model fine-tuning for innocent users compared to baseline schemes .

The dataset used for quantitative evaluation in the study is the "Logo dataset" . The code for the project is open source and can be accessed on GitHub at the following link: https://github.com/YoongiKim/AutoCrawler .

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces a novel technique called FreezeAsGuard, which aims to mitigate illegal adaptation of diffusion models by selectively freezing critical model tensors essential for fine-tuning in illegal domains . The experiments conducted demonstrate the effectiveness of FreezeAsGuard in comparison to existing model unlearning schemes like UCE and IMMA . The results show that FreezeAsGuard consistently outperforms these baseline schemes across different diffusion models, indicating the efficacy of the proposed approach .

Furthermore, the paper discusses the importance of tensor freezing in preventing users from reversing mitigation maneuvers applied to diffusion models, emphasizing the need to constrain the trainability of model tensors during fine-tuning . The experiments validate this concept by showcasing how FreezeAsGuard limits the representation power of diffusion models in illegal domains without compromising their performance in innocent domains . This aligns with the scientific hypothesis that selectively freezing critical tensors can effectively prevent illegal adaptation while maintaining model performance in legitimate domains.

Moreover, the quantitative results presented in the paper, such as the reduction in Frechet Inception Distance (FID) in illegal domains and the improvement in CLIP scores in innocent domains, provide concrete evidence supporting the effectiveness of FreezeAsGuard . The experiments also explore the impact of different scales of illegal domains and innocent domains on the mitigation power of FreezeAsGuard, demonstrating its robustness across various scenarios . Additionally, the comparison with random freezing strategies further highlights the superiority of FreezeAsGuard in mitigating illegal adaptation while preserving model performance .

In conclusion, the experiments and results presented in the paper offer compelling evidence to validate the scientific hypotheses underlying the development and efficacy of FreezeAsGuard in mitigating illegal adaptation of diffusion models. The consistent performance improvements observed across different diffusion models and domains underscore the robustness and effectiveness of the proposed technique, providing strong support for the scientific hypotheses put forth in the study.

The paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" makes the following contributions:

• Introduces FreezeAsGuard, a technique designed to mitigate illegal adaptation of diffusion models by selectively freezing model tensors crucial for illegal domains, outperforming existing model unlearning schemes .
• Demonstrates that FreezeAsGuard consistently outperforms baseline schemes across all diffusion models, with better performance in innocent domains for SD v1.4 and v1.5 models compared to v2.1 models .
• Proposes a method of partial model fine-tuning, highlighting the importance of fine-tuning all model components for diffusion models to maintain performance in both illegal and innocent domains .
• Shows that FreezeAsGuard can significantly reduce the FID of generated images compared to full fine-tuning, especially with different scales of illegal domains, demonstrating its mitigation power .
• Provides insights into the impact of different diffusion models, revealing that FreezeAsGuard consistently outperforms baseline schemes across all models, showcasing its effectiveness in mitigating illegal adaptations .

To delve deeper into the topic of mitigating illegal adaptation of diffusion models, further research can be conducted in the following areas:

1. Enhancing Content Filtering Techniques: Research can focus on improving content filtering methods to effectively detect and prevent illegal model adaptations. This could involve developing more robust algorithms that can accurately identify and filter out inappropriate or unauthorized content .

2. Exploring Advanced Model Unlearning Strategies: Further investigation into advanced model unlearning techniques could be beneficial. This research could aim to develop innovative approaches that can effectively erase the knowledge of illegal model adaptations from diffusion models, making it challenging for users to relearn such knowledge with custom data .

3. Studying the Impact of Tensor Freezing: Research can be conducted to analyze the implications of selective tensor freezing on diffusion models. This could involve studying the specific tensors that are critical to illegal model adaptations and understanding how freezing these tensors affects the model's representation power in illegal domains while minimizing the impact on legal model adaptations in other domains .

By exploring these avenues, researchers can contribute to the development of more effective strategies and techniques for mitigating illegal adaptations of diffusion models, thereby enhancing the security and integrity of these models in various applications.