Hyper Adversarial Tuning (HyperAT): Boosting Adversarial Robustness in Pretrained Large Vision Models Using Shared Defensive Knowledge

Temel Kavramlar

Hyper Adversarial Tuning (HyperAT) leverages shared defensive knowledge among different adversarial training methods to efficiently improve the robustness of pretrained large vision models against adversarial attacks.

Özet

• Bibliographic Information: Lv, K., Cao, H., Tu, K., Xu, Y., Zhang, Z., Ding, X., & Wang, Y. (2024). Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models. arXiv preprint arXiv:2410.05951v1.
• Research Objective: This paper aims to address the efficiency and effectiveness challenges of traditional adversarial training for large vision models by proposing Hyper Adversarial Tuning (HyperAT), a novel robust tuning framework that leverages shared defensive knowledge among different adversarial training methods.
• Methodology: HyperAT integrates hypernetworks with low-rank adaptation (LoRA) to generate method-specific LoRA modules for different adversarial training methods. It utilizes a shared hypernetwork to generate weights for these LoRA modules, enabling knowledge transfer between different methods. The method involves training specialist hypernetworks for each adversarial training method and then merging the generated LoRA modules to enhance robustness generalization.
• Key Findings: Experiments on CIFAR-10, CIFAR-100, and Imagenette datasets demonstrate that HyperAT significantly enhances the adversarial robustness of pretrained large vision models, such as ViT-B, ViT-L, and DeiT, compared to existing adversarial training methods and state-of-the-art parameter-efficient fine-tuning (PEFT) methods. HyperAT achieves superior robust accuracy while significantly reducing the number of trainable parameters.
• Main Conclusions: HyperAT offers a computationally efficient and highly effective approach for boosting the adversarial robustness of pretrained large vision models. The proposed framework leverages shared defensive knowledge among different adversarial training methods, leading to improved robustness generalization and reduced computational overhead.
• Significance: This research contributes to the field of adversarial robustness in computer vision by proposing a novel and effective method for enhancing the resilience of large vision models against adversarial attacks. The parameter efficiency of HyperAT makes it particularly suitable for real-world applications where computational resources are limited.
• Limitations and Future Research: The authors acknowledge that the specific combination of adversarial training methods used in HyperAT can influence its overall performance. Future research could explore optimal method combinations and investigate the applicability of HyperAT to other types of deep learning models beyond vision transformers.

İstatistikler

HyperAT boosts overall robust accuracy by approximately 7–8% under various attack evaluations compared to Vanilla AT.
For ViT-L, the proportion of additional trainable parameters introduced by HyperAT is even smaller compared to that in ViT-B, decreasing from 18.98% to 12.65%.
Increasing the rank (r) of LoRA from 8 to 32 in HyperAT leads to performance gain, but the improvement becomes marginal beyond r = 32.

Alıntılar

"To simultaneously address the efficiency and effectiveness challenges in conventional adversarial training for large vision models, we propose HyperAT, a novel robust tuning framework, by introducing a shared Hypernetwork and a mixture of defensive LoRAs into adversarial tuning."
"Our method is readily compatible and extensible to other adversarial training methods. By incorporating more advanced and powerful training methods, the overall model performance can be further enhanced without introducing excessive computational overhead."
"With extensive experiments on three benchmark datasets, we demonstrate the superiority of HyperAT over existing state-of-the-art adversarial defenses. Notably, HyperAT can even surpass the robustness achieved by fully fine-tuning the entire model while requiring substantially fewer trainable parameters."

Önemli Bilgiler Şuradan Elde Edildi

Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models

by Kangtao Lv, ... : arxiv.org 10-10-2024

https://arxiv.org/pdf/2410.05951.pdf

Daha Derin Sorular

How does HyperAT compare to other emerging adversarial defense techniques beyond the ones mentioned in the paper, and what are the potential synergies between these methods?

HyperAT presents a novel approach to adversarial defense by combining hypernetworks, LoRA, and model merging. However, the field of adversarial defense is constantly evolving, with several other promising techniques emerging. Let's explore some of these techniques and their potential synergies with HyperAT:

Adversarial Training with Generative Models: Techniques like Generative Adversarial Training (GAT) leverage generative adversarial networks (GANs) to generate more diverse and realistic adversarial examples, potentially leading to more robust models. Integrating GAT with HyperAT could involve using a GAN to generate adversarial examples for each defense method, further diversifying the training data and potentially improving generalization.

Robustness Certification: Methods like randomized smoothing provide provable guarantees of robustness by smoothing the decision boundary of a model. Combining HyperAT with certification techniques could involve using HyperAT to train a base model and then applying randomized smoothing to achieve certified robustness, potentially leading to more trustworthy AI systems.

Ensemble Diversity: While HyperAT leverages model merging, other ensemble methods focus on promoting diversity among ensemble members. Techniques like Deep Ensemble Adversarial Training (DEAT) encourage diversity in adversarial example generation within the ensemble. Integrating DEAT with HyperAT could involve using DEAT to train the individual specialist hypernetworks, further enhancing the diversity of the merged model and potentially improving robustness.

Context-Aware Defenses: Emerging techniques recognize the importance of context in adversarial robustness. For instance, methods like Spatially Adaptive Adversarial Training (SAAT) adapt the defense strategy based on the spatial context of the input image. Integrating SAAT with HyperAT could involve incorporating spatial information into the hypernetwork's input, allowing it to generate LoRA modules tailored to different spatial regions of the input, potentially leading to more effective defenses against spatially targeted attacks.
Synergies and Future Directions:
The potential synergies between HyperAT and these emerging techniques highlight the exciting possibilities for developing more robust and trustworthy AI systems. By combining the strengths of different approaches, we can potentially overcome the limitations of individual methods and achieve significant advancements in adversarial defense. Future research could explore these synergies further, investigating optimal ways to integrate these techniques and develop novel hybrid defense mechanisms.

Could the reliance on pre-trained models and their inherent biases limit the generalizability of HyperAT's robustness enhancements when applied to real-world scenarios with diverse and potentially unseen data distributions?

You raise a valid concern. While HyperAT demonstrates promising results in enhancing the adversarial robustness of pre-trained models, its reliance on these models does introduce potential limitations regarding generalizability in real-world scenarios:

Domain Shift and Data Bias: Pre-trained models are often trained on massive datasets, but these datasets may not fully represent the diversity of real-world data. Consequently, the pre-trained features and the robustness enhancements achieved by HyperAT might not generalize well to unseen data distributions or domains significantly different from the training data.

Bias Amplification: Pre-trained models can inherit biases present in their training data. Applying HyperAT might inadvertently amplify these biases, leading to unfair or discriminatory outcomes when deployed in real-world applications, especially in sensitive domains like healthcare or finance.

Overfitting to Adversarial Examples: While HyperAT aims to improve robustness against various attacks, it primarily relies on adversarial examples generated from known attack methods. This reliance could lead to overfitting to these specific attacks, making the model vulnerable to unseen or more sophisticated attacks in real-world scenarios.
Mitigating the Limitations:
Addressing these limitations is crucial for deploying HyperAT in real-world applications:

Domain Adaptation and Fine-tuning: Fine-tuning the pre-trained model and the HyperAT hypernetwork on data from the target domain can help bridge the domain gap and improve generalization.

Bias Mitigation Techniques: Incorporating bias mitigation techniques during pre-training, fine-tuning, or within the HyperAT framework can help reduce bias amplification and promote fairness.

Diverse Adversarial Training: Expanding the diversity of adversarial examples used during training, potentially by incorporating generative methods or leveraging real-world attack data, can improve robustness against unseen attacks.

Continual Learning and Adaptation: Developing methods for continual learning and adaptation within the HyperAT framework can enable the model to adapt to evolving data distributions and emerging attack strategies.

What are the broader implications of achieving robust and trustworthy AI systems, particularly in safety-critical applications where the consequences of adversarial attacks can be significant?

Achieving robust and trustworthy AI systems, particularly in safety-critical applications, holds profound implications for our society:

Enhanced Safety and Reliability: In domains like autonomous driving, healthcare, and aerospace, AI systems are entrusted with critical decision-making. Robust AI, resilient to adversarial attacks, is paramount to ensure the safety and reliability of these systems, preventing potentially life-threatening consequences.

Increased Trust and Adoption: Widespread adoption of AI hinges on trust in its capabilities. Robust AI systems, demonstrably resistant to manipulation, foster trust among users and stakeholders, paving the way for broader integration of AI into various aspects of our lives.

Fairness and Equity: Robust AI systems, less susceptible to bias amplification, can contribute to fairer and more equitable outcomes. This is particularly crucial in applications like loan approval, hiring processes, and criminal justice, where biased AI can perpetuate and exacerbate existing societal inequalities.

Security and Defense: In an increasingly interconnected world, robust AI is essential for national security and defense. AI systems used in surveillance, threat detection, and autonomous weapons systems must be resilient to adversarial attacks to prevent catastrophic consequences.

Ethical and Responsible AI Development: The pursuit of robust AI aligns with the broader goals of ethical and responsible AI development. By prioritizing robustness, we strive to create AI systems that are reliable, fair, and accountable, mitigating potential harms and maximizing societal benefits.
Realizing the Potential:
Achieving robust and trustworthy AI is an ongoing endeavor. It requires collaborative efforts from researchers, developers, policymakers, and society as a whole. By investing in research, establishing robust evaluation benchmarks, fostering transparency and accountability, and promoting ethical guidelines, we can pave the way for a future where AI systems are not only intelligent but also trustworthy, reliable, and beneficial for all.